From ec963b76e067546c4aa6063ea3879bb01ef41843 Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Mon, 1 Nov 2021 16:50:26 +0100
Subject: [PATCH] update

---
 .../_build/jupyter_execute/Clustering.ipynb   |   82 +-
 doc/pub/week34/ipynb/week34.ipynb             | 1269 +++++++++++---
 doc/pub/week38/ipynb/week38.ipynb             |  748 ++++++--
 doc/pub/week44/html/week44-bs.html            |  308 ++--
 doc/pub/week44/html/week44-reveal.html        | 1337 +++++++++------
 doc/pub/week44/html/week44-solarized.html     | 1329 ++++++++++-----
 doc/pub/week44/html/week44.html               | 1407 +++++++++------
 doc/pub/week44/ipynb/ipynb-week44-src.tar.gz  |  Bin 293594 -> 294283 bytes
 doc/pub/week44/ipynb/week44.ipynb             | 1510 +++++++----------
 doc/src/week44/week44.do.txt                  |    4 +-
 10 files changed, 5155 insertions(+), 2839 deletions(-)

diff --git a/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb b/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb
index f54a64a96..5974be6d2 100644
--- a/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2150517f",
+   "id": "04229efd",
    "metadata": {},
    "source": [
     "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6e0cd96",
+   "id": "e2e714e4",
    "metadata": {},
    "source": [
     "# Clustering Analysis\n",
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e755ea79",
+   "id": "1466c1cb",
    "metadata": {},
    "source": [
     "## Basic Idea of the K-means Clustering Algorithm\n",
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "682f37d4",
+   "id": "30580c15",
    "metadata": {},
    "source": [
     "<!-- Equation labels as ordinary links -->\n",
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3bd80ebf",
+   "id": "c430172a",
    "metadata": {},
    "source": [
     "which we wish to group into $K < n$ clusters. For our dissimilarity measure we\n",
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c34b3e47",
+   "id": "885eba7f",
    "metadata": {},
    "source": [
     "<!-- Equation labels as ordinary links -->\n",
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54897ef5",
+   "id": "485a4b5c",
    "metadata": {},
    "source": [
     "Next we define the so called *within-cluster point scatter* which gives us a\n",
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02ec7cdd",
+   "id": "1d3c6ca7",
    "metadata": {},
    "source": [
     "<!-- Equation labels as ordinary links -->\n",
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d15918ea",
+   "id": "33a14233",
    "metadata": {},
    "source": [
     "where $\\boldsymbol{\\overline{x_k}}$ is the mean vector associated with the $k$-th\n",
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8576296d",
+   "id": "7044f7fc",
    "metadata": {},
    "source": [
     "<!-- Equation labels as ordinary links -->\n",
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2736464c",
+   "id": "7d612869",
    "metadata": {},
    "source": [
     "Which is a quantity that is conserved throughout the $k$-means algorithm. It can\n",
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bbe04304",
+   "id": "3dc3f01f",
    "metadata": {},
    "source": [
     "<!-- Equation labels as ordinary links -->\n",
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e8910ec",
+   "id": "f51d30c2",
    "metadata": {},
    "source": [
     "Now we have all the pieces necessary to formally revisit the k-means algorithm.\n",
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb548d32",
+   "id": "9265c30e",
    "metadata": {},
    "source": [
     "## The K-means Clustering Algorithm\n",
@@ -244,7 +244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc569914",
+   "id": "63dc40f2",
    "metadata": {},
    "source": [
     "## Writing Our Own Code\n",
@@ -255,7 +255,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8601ba47",
+   "id": "9e110f91",
    "metadata": {},
    "source": [
     "### Basic Python\n",
@@ -277,7 +277,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "3023fb96",
+   "id": "c5196fbb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -294,7 +294,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18feed32",
+   "id": "46f6bf75",
    "metadata": {},
    "source": [
     "Next we define functions, for ease of use later, to generate Gaussians and to\n",
@@ -304,7 +304,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "171b8aa5",
+   "id": "c5db2b9c",
    "metadata": {},
    "outputs": [
     {
@@ -377,7 +377,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22a5cc09",
+   "id": "123f8b0a",
    "metadata": {},
    "source": [
     "Now that we are our, albeit very simple, dataset we are ready to start\n",
@@ -387,7 +387,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "7ef1f112",
+   "id": "44d87c0c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -428,7 +428,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6fe8327e",
+   "id": "b0bf3a01",
    "metadata": {},
    "source": [
     "Let's plot and see"
@@ -437,7 +437,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "79427d57",
+   "id": "4af50c01",
    "metadata": {},
    "outputs": [
     {
@@ -474,7 +474,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d14cf57",
+   "id": "9a370b9d",
    "metadata": {},
    "source": [
     "So what do we have so far? We have 'picked' $k$ centroids at random from our\n",
@@ -492,7 +492,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "4c9c9a53",
+   "id": "7e049bf1",
    "metadata": {},
    "outputs": [
     {
@@ -500,7 +500,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration 5\n",
-      "Runtime: 0.24420595169067383 seconds\n"
+      "Runtime: 0.2396700382232666 seconds\n"
      ]
     }
    ],
@@ -557,7 +557,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61f30110",
+   "id": "2602cc83",
    "metadata": {},
    "source": [
     "And thats it! We now have an extremely barebones, un-optimized k-means\n",
@@ -567,7 +567,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "d964670a",
+   "id": "743467eb",
    "metadata": {},
    "outputs": [
     {
@@ -604,7 +604,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "351ef866",
+   "id": "1f2749e0",
    "metadata": {},
    "source": [
     "Now there are a few glaring improvements to be done here. First of all is\n",
@@ -617,7 +617,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d80d4b2",
+   "id": "ac9fa4a5",
    "metadata": {},
    "source": [
     "## Towards a More Numpythonic Code"
@@ -626,7 +626,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "a88b26c9",
+   "id": "9edf7db3",
    "metadata": {},
    "outputs": [
     {
@@ -634,7 +634,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 5\n",
-      "Runtime: 0.20550775527954102 seconds\n"
+      "Runtime: 0.20168113708496094 seconds\n"
      ]
     }
    ],
@@ -748,7 +748,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7372c09e",
+   "id": "212c648e",
    "metadata": {},
    "source": [
     "**Note**: the start of the timing is after the random initialization, and first\n",
@@ -769,7 +769,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "566f0e04",
+   "id": "07bfff3f",
    "metadata": {},
    "outputs": [
     {
@@ -777,7 +777,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 11\n",
-      "Runtime: 0.38791799545288086 seconds\n",
+      "Runtime: 0.389873743057251 seconds\n",
       " "
      ]
     }
@@ -789,7 +789,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44ae7ea9",
+   "id": "b7f27a4a",
    "metadata": {},
    "source": [
     "Here we can see the reason for profiling. We now know for certain a lot can be\n",
@@ -801,7 +801,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "93b58a7f",
+   "id": "b9ffa227",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -892,7 +892,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2ce6830",
+   "id": "da6c65d3",
    "metadata": {},
    "source": [
     "When working towards becoming a data scientist using Python this last step is\n",
@@ -905,7 +905,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "cd0e1669",
+   "id": "ea31860d",
    "metadata": {},
    "outputs": [
     {
@@ -913,7 +913,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 5\n",
-      "Runtime: 0.0023779869079589844 seconds\n"
+      "Runtime: 0.002451181411743164 seconds\n"
      ]
     }
    ],
@@ -924,7 +924,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d86b2a09",
+   "id": "2a34f7af",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/doc/pub/week34/ipynb/week34.ipynb b/doc/pub/week34/ipynb/week34.ipynb
index 87019367d..3c16b014b 100644
--- a/doc/pub/week34/ipynb/week34.ipynb
+++ b/doc/pub/week34/ipynb/week34.ipynb
@@ -818,11 +818,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np"
@@ -837,12 +834,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.64974716  1.01709028 -2.25728684 -1.70030371 -0.91063346  0.28585046\n",
+      " -1.75211622  0.60463306 -1.91066485  0.46111841]\n"
+     ]
+    }
+   ],
    "source": [
     "n = 10\n",
     "x = np.random.normal(size=n)\n",
@@ -859,12 +862,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "x = np.array([1, 2, 3])\n",
@@ -881,12 +889,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.38629436 1.94591015 2.07944154]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "x = np.log(np.array([4, 7, 8]))\n",
@@ -908,12 +921,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 1 2]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from math import log\n",
@@ -933,12 +951,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.38629436 1.94591015 2.07944154]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "x = np.log(np.array([4, 7, 8], dtype = np.float64))\n",
@@ -954,12 +977,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.38629436 1.94591015 2.07944154]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "x = np.log(np.array([4.0, 7.0, 8.0]))\n",
@@ -975,12 +1003,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "x = np.log(np.array([4.0, 7.0, 8.0]))\n",
@@ -1000,12 +1033,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1.38629436 1.94591015 2.07944154]\n",
+      " [1.09861229 2.30258509 2.39789527]\n",
+      " [1.38629436 1.60943791 1.94591015]]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))\n",
@@ -1021,12 +1061,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.38629436 1.09861229 1.38629436]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))\n",
@@ -1043,12 +1088,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.09861229 2.30258509 2.39789527]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))\n",
@@ -1065,12 +1115,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "n = 10\n",
@@ -1088,12 +1152,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "n = 10\n",
@@ -1111,12 +1189,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.68218216 0.27644788 0.9833141  0.7016604  0.28372853 0.03963638\n",
+      "  0.31235643 0.78463128 0.58009105 0.00635777]\n",
+      " [0.17004415 0.48869163 0.67191208 0.972085   0.58799781 0.67823872\n",
+      "  0.09510447 0.60073299 0.09359946 0.04149216]\n",
+      " [0.71921211 0.7351347  0.15652074 0.48198418 0.73649181 0.36848887\n",
+      "  0.16245414 0.71223944 0.6160587  0.23878855]\n",
+      " [0.86407756 0.2117915  0.40293704 0.95485525 0.56906986 0.67295828\n",
+      "  0.3359371  0.99758915 0.08401521 0.1793366 ]\n",
+      " [0.83845734 0.81320978 0.47165213 0.09342382 0.57739086 0.04620871\n",
+      "  0.88725273 0.02267528 0.0706586  0.5756085 ]\n",
+      " [0.82298756 0.50036637 0.48393363 0.45014937 0.19973586 0.10558537\n",
+      "  0.09622351 0.89489918 0.15100071 0.2138819 ]\n",
+      " [0.84016119 0.00306112 0.76693891 0.45775191 0.72486852 0.01153126\n",
+      "  0.91720466 0.22186325 0.8388034  0.13748175]\n",
+      " [0.22517005 0.82576694 0.14841941 0.10291046 0.62870179 0.76410459\n",
+      "  0.44756695 0.92248196 0.03615603 0.1642297 ]\n",
+      " [0.43451166 0.54397762 0.46641279 0.49050851 0.05319184 0.44656514\n",
+      "  0.83622352 0.30754567 0.07568043 0.45514686]\n",
+      " [0.09645203 0.34213209 0.74727052 0.27602613 0.89649455 0.99466209\n",
+      "  0.24340612 0.51516454 0.64974914 0.59602013]]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "n = 10\n",
@@ -1195,12 +1297,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.09215183897865517\n",
+      "4.24403991539569\n",
+      "0.16335237122489882\n",
+      "[[ 1.35209255  3.94161435  4.15878012]\n",
+      " [ 3.94161435 12.61939998 12.38102341]\n",
+      " [ 4.15878012 12.38102341 17.35220435]]\n",
+      "[28.7826315   0.0977036   2.44336178]\n"
+     ]
+    }
+   ],
    "source": [
     "# Importing various packages\n",
     "import numpy as np\n",
@@ -1221,12 +1334,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'scipy'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_12115/1724975172.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msparse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0meye\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meye\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meye\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'scipy'"
+     ]
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "\n",
@@ -1267,12 +1389,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>First Name</th>\n",
+       "      <th>Last Name</th>\n",
+       "      <th>Place of birth</th>\n",
+       "      <th>Date of Birth T.A.</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Frodo</td>\n",
+       "      <td>Baggins</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bilbo</td>\n",
+       "      <td>Baggins</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Aragorn II</td>\n",
+       "      <td>Elessar</td>\n",
+       "      <td>Eriador</td>\n",
+       "      <td>2931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Samwise</td>\n",
+       "      <td>Gamgee</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2980</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   First Name Last Name Place of birth  Date of Birth T.A.\n",
+       "0       Frodo   Baggins          Shire                2968\n",
+       "1       Bilbo   Baggins          Shire                2890\n",
+       "2  Aragorn II   Elessar        Eriador                2931\n",
+       "3     Samwise    Gamgee          Shire                2980"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "from IPython.display import display\n",
@@ -1297,12 +1488,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>First Name</th>\n",
+       "      <th>Last Name</th>\n",
+       "      <th>Place of birth</th>\n",
+       "      <th>Date of Birth T.A.</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Frodo</th>\n",
+       "      <td>Frodo</td>\n",
+       "      <td>Baggins</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bilbo</th>\n",
+       "      <td>Bilbo</td>\n",
+       "      <td>Baggins</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aragorn</th>\n",
+       "      <td>Aragorn II</td>\n",
+       "      <td>Elessar</td>\n",
+       "      <td>Eriador</td>\n",
+       "      <td>2931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sam</th>\n",
+       "      <td>Samwise</td>\n",
+       "      <td>Gamgee</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2980</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         First Name Last Name Place of birth  Date of Birth T.A.\n",
+       "Frodo         Frodo   Baggins          Shire                2968\n",
+       "Bilbo         Bilbo   Baggins          Shire                2890\n",
+       "Aragorn  Aragorn II   Elessar        Eriador                2931\n",
+       "Sam         Samwise    Gamgee          Shire                2980"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])\n",
     "display(data_pandas)"
@@ -1317,12 +1577,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "First Name            Aragorn II\n",
+       "Last Name                Elessar\n",
+       "Place of birth           Eriador\n",
+       "Date of Birth T.A.          2931\n",
+       "Name: Aragorn, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "display(data_pandas.loc['Aragorn'])"
    ]
@@ -1336,12 +1607,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>First Name</th>\n",
+       "      <th>Last Name</th>\n",
+       "      <th>Place of birth</th>\n",
+       "      <th>Date of Birth T.A.</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Frodo</th>\n",
+       "      <td>Frodo</td>\n",
+       "      <td>Baggins</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bilbo</th>\n",
+       "      <td>Bilbo</td>\n",
+       "      <td>Baggins</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aragorn</th>\n",
+       "      <td>Aragorn II</td>\n",
+       "      <td>Elessar</td>\n",
+       "      <td>Eriador</td>\n",
+       "      <td>2931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sam</th>\n",
+       "      <td>Samwise</td>\n",
+       "      <td>Gamgee</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pippin</th>\n",
+       "      <td>Peregrin</td>\n",
+       "      <td>Took</td>\n",
+       "      <td>Shire</td>\n",
+       "      <td>2990</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         First Name Last Name Place of birth  Date of Birth T.A.\n",
+       "Frodo         Frodo   Baggins          Shire                2968\n",
+       "Bilbo         Bilbo   Baggins          Shire                2890\n",
+       "Aragorn  Aragorn II   Elessar        Eriador                2931\n",
+       "Sam         Samwise    Gamgee          Shire                2980\n",
+       "Pippin     Peregrin      Took          Shire                2990"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "new_hobbit = {'First Name': [\"Peregrin\"],\n",
     "              'Last Name': [\"Took\"],\n",
@@ -1362,12 +1710,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.749765</td>\n",
+       "      <td>0.342680</td>\n",
+       "      <td>1.153036</td>\n",
+       "      <td>-0.252436</td>\n",
+       "      <td>0.981321</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.514219</td>\n",
+       "      <td>0.221180</td>\n",
+       "      <td>-1.070043</td>\n",
+       "      <td>-0.189496</td>\n",
+       "      <td>0.255001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.458027</td>\n",
+       "      <td>0.435163</td>\n",
+       "      <td>-0.583595</td>\n",
+       "      <td>0.816847</td>\n",
+       "      <td>0.672721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.104411</td>\n",
+       "      <td>-0.531280</td>\n",
+       "      <td>1.029733</td>\n",
+       "      <td>-0.438136</td>\n",
+       "      <td>-1.118318</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.618982</td>\n",
+       "      <td>1.541605</td>\n",
+       "      <td>-0.251879</td>\n",
+       "      <td>-0.842436</td>\n",
+       "      <td>0.184519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.937082</td>\n",
+       "      <td>0.731000</td>\n",
+       "      <td>1.361556</td>\n",
+       "      <td>-0.326238</td>\n",
+       "      <td>0.055676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.222400</td>\n",
+       "      <td>-1.443217</td>\n",
+       "      <td>-0.756352</td>\n",
+       "      <td>0.816454</td>\n",
+       "      <td>0.750445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-0.455947</td>\n",
+       "      <td>1.189622</td>\n",
+       "      <td>-1.690617</td>\n",
+       "      <td>-1.356399</td>\n",
+       "      <td>-1.232435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-0.544439</td>\n",
+       "      <td>-0.668172</td>\n",
+       "      <td>0.007315</td>\n",
+       "      <td>-0.612939</td>\n",
+       "      <td>1.299748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-1.733096</td>\n",
+       "      <td>-0.983310</td>\n",
+       "      <td>0.357508</td>\n",
+       "      <td>-1.613579</td>\n",
+       "      <td>1.470714</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         1         2         3         4\n",
+       "0 -1.749765  0.342680  1.153036 -0.252436  0.981321\n",
+       "1  0.514219  0.221180 -1.070043 -0.189496  0.255001\n",
+       "2 -0.458027  0.435163 -0.583595  0.816847  0.672721\n",
+       "3 -0.104411 -0.531280  1.029733 -0.438136 -1.118318\n",
+       "4  1.618982  1.541605 -0.251879 -0.842436  0.184519\n",
+       "5  0.937082  0.731000  1.361556 -0.326238  0.055676\n",
+       "6  0.222400 -1.443217 -0.756352  0.816454  0.750445\n",
+       "7 -0.455947  1.189622 -1.690617 -1.356399 -1.232435\n",
+       "8 -0.544439 -0.668172  0.007315 -0.612939  1.299748\n",
+       "9 -1.733096 -0.983310  0.357508 -1.613579  1.470714"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "0   -0.175300\n",
+      "1    0.083527\n",
+      "2   -0.044334\n",
+      "3   -0.399836\n",
+      "4    0.331939\n",
+      "dtype: float64\n",
+      "0    1.069584\n",
+      "1    0.965548\n",
+      "2    1.018232\n",
+      "3    0.793167\n",
+      "4    0.918992\n",
+      "dtype: float64\n"
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+    {
+     "data": {
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+       "      <td>0.063724</td>\n",
+       "      <td>0.962990</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.264421</td>\n",
+       "      <td>0.048920</td>\n",
+       "      <td>1.144993</td>\n",
+       "      <td>0.035909</td>\n",
+       "      <td>0.065026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.209789</td>\n",
+       "      <td>0.189367</td>\n",
+       "      <td>0.340583</td>\n",
+       "      <td>0.667239</td>\n",
+       "      <td>0.452553</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.010902</td>\n",
+       "      <td>0.282259</td>\n",
+       "      <td>1.060349</td>\n",
+       "      <td>0.191963</td>\n",
+       "      <td>1.250636</td>\n",
+       "    </tr>\n",
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+       "      <th>4</th>\n",
+       "      <td>2.621102</td>\n",
+       "      <td>2.376547</td>\n",
+       "      <td>0.063443</td>\n",
+       "      <td>0.709698</td>\n",
+       "      <td>0.034047</td>\n",
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+       "      <th>5</th>\n",
+       "      <td>0.878123</td>\n",
+       "      <td>0.534362</td>\n",
+       "      <td>1.853835</td>\n",
+       "      <td>0.106431</td>\n",
+       "      <td>0.003100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.049462</td>\n",
+       "      <td>2.082875</td>\n",
+       "      <td>0.572069</td>\n",
+       "      <td>0.666597</td>\n",
+       "      <td>0.563167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.207888</td>\n",
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+       "      <td>1.839818</td>\n",
+       "      <td>1.518895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>0.296414</td>\n",
+       "      <td>0.446453</td>\n",
+       "      <td>0.000054</td>\n",
+       "      <td>0.375694</td>\n",
+       "      <td>1.689345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>3.003620</td>\n",
+       "      <td>0.966899</td>\n",
+       "      <td>0.127812</td>\n",
+       "      <td>2.603636</td>\n",
+       "      <td>2.162999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         1         2         3         4\n",
+       "0  3.061679  0.117430  1.329492  0.063724  0.962990\n",
+       "1  0.264421  0.048920  1.144993  0.035909  0.065026\n",
+       "2  0.209789  0.189367  0.340583  0.667239  0.452553\n",
+       "3  0.010902  0.282259  1.060349  0.191963  1.250636\n",
+       "4  2.621102  2.376547  0.063443  0.709698  0.034047\n",
+       "5  0.878123  0.534362  1.853835  0.106431  0.003100\n",
+       "6  0.049462  2.082875  0.572069  0.666597  0.563167\n",
+       "7  0.207888  1.415201  2.858185  1.839818  1.518895\n",
+       "8  0.296414  0.446453  0.000054  0.375694  1.689345\n",
+       "9  3.003620  0.966899  0.127812  2.603636  2.162999"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -1393,12 +2017,189 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
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+       "\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>First</th>\n",
+       "      <th>Second</th>\n",
+       "      <th>Third</th>\n",
+       "      <th>Fourth</th>\n",
+       "      <th>Fifth</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.749765</td>\n",
+       "      <td>0.342680</td>\n",
+       "      <td>1.153036</td>\n",
+       "      <td>-0.252436</td>\n",
+       "      <td>0.981321</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.514219</td>\n",
+       "      <td>0.221180</td>\n",
+       "      <td>-1.070043</td>\n",
+       "      <td>-0.189496</td>\n",
+       "      <td>0.255001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.458027</td>\n",
+       "      <td>0.435163</td>\n",
+       "      <td>-0.583595</td>\n",
+       "      <td>0.816847</td>\n",
+       "      <td>0.672721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.104411</td>\n",
+       "      <td>-0.531280</td>\n",
+       "      <td>1.029733</td>\n",
+       "      <td>-0.438136</td>\n",
+       "      <td>-1.118318</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.618982</td>\n",
+       "      <td>1.541605</td>\n",
+       "      <td>-0.251879</td>\n",
+       "      <td>-0.842436</td>\n",
+       "      <td>0.184519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.937082</td>\n",
+       "      <td>0.731000</td>\n",
+       "      <td>1.361556</td>\n",
+       "      <td>-0.326238</td>\n",
+       "      <td>0.055676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.222400</td>\n",
+       "      <td>-1.443217</td>\n",
+       "      <td>-0.756352</td>\n",
+       "      <td>0.816454</td>\n",
+       "      <td>0.750445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-0.455947</td>\n",
+       "      <td>1.189622</td>\n",
+       "      <td>-1.690617</td>\n",
+       "      <td>-1.356399</td>\n",
+       "      <td>-1.232435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-0.544439</td>\n",
+       "      <td>-0.668172</td>\n",
+       "      <td>0.007315</td>\n",
+       "      <td>-0.612939</td>\n",
+       "      <td>1.299748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-1.733096</td>\n",
+       "      <td>-0.983310</td>\n",
+       "      <td>0.357508</td>\n",
+       "      <td>-1.613579</td>\n",
+       "      <td>1.470714</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      First    Second     Third    Fourth     Fifth\n",
+       "0 -1.749765  0.342680  1.153036 -0.252436  0.981321\n",
+       "1  0.514219  0.221180 -1.070043 -0.189496  0.255001\n",
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+       "3 -0.104411 -0.531280  1.029733 -0.438136 -1.118318\n",
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+       "8 -0.544439 -0.668172  0.007315 -0.612939  1.299748\n",
+       "9 -1.733096 -0.983310  0.357508 -1.613579  1.470714"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.08352721390288316\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 10 entries, 0 to 9\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column  Non-Null Count  Dtype  \n",
+      "---  ------  --------------  -----  \n",
+      " 0   First   10 non-null     float64\n",
+      " 1   Second  10 non-null     float64\n",
+      " 2   Third   10 non-null     float64\n",
+      " 3   Fourth  10 non-null     float64\n",
+      " 4   Fifth   10 non-null     float64\n",
+      "dtypes: float64(5)\n",
+      "memory usage: 480.0 bytes\n",
+      "None\n",
+      "           First     Second      Third     Fourth      Fifth\n",
+      "count  10.000000  10.000000  10.000000  10.000000  10.000000\n",
+      "mean   -0.175300   0.083527  -0.044334  -0.399836   0.331939\n",
+      "std     1.069584   0.965548   1.018232   0.793167   0.918992\n",
+      "min    -1.749765  -1.443217  -1.690617  -1.613579  -1.232435\n",
+      "25%    -0.522836  -0.633949  -0.713163  -0.785061   0.087887\n",
+      "50%    -0.280179   0.281930  -0.122282  -0.382187   0.463861\n",
+      "75%     0.441264   0.657041   0.861676  -0.205231   0.923602\n",
+      "max     1.618982   1.541605   1.361556   0.816847   1.470714\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']\n",
     "df.index = np.arange(10)\n",
@@ -1429,12 +2230,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0  1  2  3]\n",
+      " [ 4  5  6  7]\n",
+      " [ 8  9 10 11]\n",
+      " [12 13 14 15]]\n",
+      "    0   1   2   3\n",
+      "0   0   1   2   3\n",
+      "1   4   5   6   7\n",
+      "2   8   9  10  11\n",
+      "3  12  13  14  15\n"
+     ]
+    }
+   ],
    "source": [
     "b = np.arange(16).reshape((4,4))\n",
     "print(b)\n",
@@ -1528,12 +2342,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'sklearn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_12115/2268754013.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'"
+     ]
+    }
+   ],
    "source": [
     "# Importing various packages\n",
     "import numpy as np\n",
@@ -1661,10 +2484,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1706,10 +2526,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np \n",
@@ -1882,10 +2699,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -2089,10 +2903,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Common imports\n",
@@ -2140,10 +2951,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from pylab import plt, mpl\n",
@@ -2176,10 +2984,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "\"\"\"                                                                                                                         \n",
@@ -2207,10 +3012,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Read the experimental data with Pandas\n",
@@ -2252,10 +3054,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "A = Masses['A']\n",
@@ -2277,10 +3076,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Now we set up the design matrix X\n",
@@ -2302,10 +3098,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "clf = skl.LinearRegression().fit(X, Energies)\n",
@@ -2323,10 +3116,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# The mean squared error                               \n",
@@ -2363,10 +3153,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "\n",
@@ -2414,10 +3201,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.neural_network import MLPRegressor\n",
@@ -2830,10 +3614,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Common imports\n",
@@ -3336,10 +4117,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# matrix inversion to find beta\n",
@@ -3358,10 +4136,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fit = np.linalg.lstsq(X, Energies, rcond =None)[0]\n",
@@ -3378,10 +4153,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "Masses['Eapprox']  = ytilde\n",
@@ -3411,10 +4183,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def R2(y_data, y_model):\n",
@@ -3431,10 +4200,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "print(R2(Energies,ytilde))"
@@ -3450,10 +4216,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def MSE(y_data,y_model):\n",
@@ -3473,10 +4236,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def RelativeError(y_data,y_model):\n",
@@ -3852,10 +4612,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Common imports\n",
@@ -3974,10 +4731,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
@@ -4135,10 +4889,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = np.random.rand(100,1)\n",
@@ -4215,10 +4966,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
@@ -4319,10 +5067,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# split in training and test data\n",
@@ -4339,10 +5084,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "scaler = StandardScaler()\n",
@@ -4370,10 +5112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.random.seed()\n",
@@ -4405,7 +5144,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 4
 }
diff --git a/doc/pub/week38/ipynb/week38.ipynb b/doc/pub/week38/ipynb/week38.ipynb
index 9fe81126d..7589c3302 100644
--- a/doc/pub/week38/ipynb/week38.ipynb
+++ b/doc/pub/week38/ipynb/week38.ipynb
@@ -237,12 +237,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "\n",
@@ -401,12 +411,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'y_train' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-0eaecb4595ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Model training, we compute the mean value of y and X\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0my_train_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mX_train_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mX_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mX_train_mean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_train\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0my_train_mean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'y_train' is not defined"
+     ]
+    }
+   ],
    "source": [
     "#Model training, we compute the mean value of y and X\n",
     "y_train_mean = np.mean(y_train)\n",
@@ -669,12 +688,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True beta: [2, 0.5, 3.7]\n",
+      "Fitted beta: [2.08376632 0.19569961 3.97898392]\n",
+      "Sklearn fitted beta: [2.08376632 0.19569961 3.97898392]\n",
+      "MSE with intercept column\n",
+      "0.004113634617443131\n",
+      "MSE with intercept column from SKL\n",
+      "0.0041136346174431284\n",
+      "Manual intercept: 2.083766322923905\n",
+      "Fitted beta (wiothout intercept): [0.19569961 3.97898392]\n",
+      "Sklearn intercept: 2.0837663229239025\n",
+      "Sklearn fitted beta (without intercept): [0.19569961 3.97898392]\n",
+      "MSE with Manual intercept\n",
+      "0.004113634617443137\n",
+      "MSE with Sklearn intercept\n",
+      "0.004113634617443135\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -836,12 +886,116 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Beta values for own Ridge implementation\n",
+      "[ 1.03032441e+00  6.28336218e-02 -6.24175744e-01  5.21169160e-02\n",
+      "  2.80847477e-01  2.12552073e-01  8.13220609e-02 -1.69634577e-02\n",
+      " -6.50846112e-02 -7.38962193e-02 -5.94226022e-02 -3.50227564e-02\n",
+      " -9.80609616e-03  1.08299273e-02  2.41882037e-02  2.93492130e-02\n",
+      "  2.64742912e-02  1.63249532e-02 -5.01831271e-05 -2.15098090e-02]\n",
+      "Beta values for Scikit-Learn Ridge implementation\n",
+      "[ 1.03032441e+00  6.28336218e-02 -6.24175744e-01  5.21169159e-02\n",
+      "  2.80847477e-01  2.12552073e-01  8.13220609e-02 -1.69634577e-02\n",
+      " -6.50846112e-02 -7.38962192e-02 -5.94226022e-02 -3.50227564e-02\n",
+      " -9.80609616e-03  1.08299273e-02  2.41882037e-02  2.93492130e-02\n",
+      "  2.64742912e-02  1.63249532e-02 -5.01831188e-05 -2.15098090e-02]\n",
+      "MSE values for own Ridge implementation\n",
+      "4.3632959224230663e-07\n",
+      "MSE values for Scikit-Learn Ridge implementation\n",
+      "4.3632959170578395e-07\n",
+      "Beta values for own Ridge implementation\n",
+      "[ 1.03630548 -0.01963611 -0.37900111 -0.07062318  0.12182967  0.16343471\n",
+      "  0.13003291  0.07490892  0.02365049 -0.01449782 -0.03814292 -0.04909093\n",
+      " -0.05009826 -0.04389027 -0.03279636 -0.01866537 -0.00289724  0.01348565\n",
+      "  0.02976145  0.04543942]\n",
+      "Beta values for Scikit-Learn Ridge implementation\n",
+      "[ 1.03630548 -0.01963611 -0.37900111 -0.07062318  0.12182967  0.16343471\n",
+      "  0.13003291  0.07490892  0.02365049 -0.01449782 -0.03814292 -0.04909093\n",
+      " -0.05009826 -0.04389027 -0.03279636 -0.01866537 -0.00289724  0.01348565\n",
+      "  0.02976145  0.04543942]\n",
+      "MSE values for own Ridge implementation\n",
+      "5.194042826527663e-06\n",
+      "MSE values for Scikit-Learn Ridge implementation\n",
+      "5.194042826866047e-06\n",
+      "Beta values for own Ridge implementation\n",
+      "[ 1.04220758 -0.10931453 -0.17641709 -0.06020587  0.02208512  0.05789007\n",
+      "  0.06491736  0.05785343  0.04537385  0.03196357  0.01969145  0.00934499\n",
+      "  0.00107405 -0.00526348 -0.00992331 -0.01318643 -0.01531845 -0.01655318\n",
+      " -0.01708852 -0.01708781]\n",
+      "Beta values for Scikit-Learn Ridge implementation\n",
+      "[ 1.04220758 -0.10931453 -0.17641709 -0.06020587  0.02208512  0.05789007\n",
+      "  0.06491736  0.05785343  0.04537385  0.03196357  0.01969145  0.00934499\n",
+      "  0.00107405 -0.00526348 -0.00992331 -0.01318643 -0.01531845 -0.01655318\n",
+      " -0.01708852 -0.01708781]\n",
+      "MSE values for own Ridge implementation\n",
+      "2.094082198965824e-05\n",
+      "MSE values for Scikit-Learn Ridge implementation\n",
+      "2.094082198964181e-05\n",
+      "Beta values for own Ridge implementation\n",
+      "[ 1.01219292 -0.06043581 -0.10391807 -0.05651951 -0.01898855  0.00312361\n",
+      "  0.01463049  0.01975848  0.02123176  0.02068067  0.01905883  0.01691985\n",
+      "  0.01458337  0.01223198  0.00996754  0.00784393  0.00588657  0.00410387\n",
+      "  0.00249435  0.00105081]\n",
+      "Beta values for Scikit-Learn Ridge implementation\n",
+      "[ 1.01219292 -0.06043581 -0.10391807 -0.05651951 -0.01898855  0.00312361\n",
+      "  0.01463049  0.01975848  0.02123176  0.02068067  0.01905883  0.01691985\n",
+      "  0.01458337  0.01223198  0.00996754  0.00784393  0.00588657  0.00410387\n",
+      "  0.00249435  0.00105081]\n",
+      "MSE values for own Ridge implementation\n",
+      "0.00031535148309585413\n",
+      "MSE values for Scikit-Learn Ridge implementation\n",
+      "0.0003153514830958115\n",
+      "Beta values for own Ridge implementation\n",
+      "[ 8.38916861e-01  1.31276579e-01  8.97497404e-03 -1.72271878e-02\n",
+      " -2.11744554e-02 -1.91492986e-02 -1.57201944e-02 -1.23002365e-02\n",
+      " -9.30466214e-03 -6.81048318e-03 -4.78184120e-03 -3.15130074e-03\n",
+      " -1.84923989e-03 -8.13661243e-04  7.46984697e-06  6.56636616e-04\n",
+      "  1.16805821e-03  1.56912044e-03  1.88168312e-03  2.12318726e-03]\n",
+      "Beta values for Scikit-Learn Ridge implementation\n",
+      "[ 8.38916861e-01  1.31276579e-01  8.97497404e-03 -1.72271878e-02\n",
+      " -2.11744554e-02 -1.91492986e-02 -1.57201944e-02 -1.23002365e-02\n",
+      " -9.30466214e-03 -6.81048318e-03 -4.78184120e-03 -3.15130074e-03\n",
+      " -1.84923989e-03 -8.13661243e-04  7.46984697e-06  6.56636616e-04\n",
+      "  1.16805821e-03  1.56912044e-03  1.88168312e-03  2.12318726e-03]\n",
+      "MSE values for own Ridge implementation\n",
+      "0.015072388895177091\n",
+      "MSE values for Scikit-Learn Ridge implementation\n",
+      "0.015072388895177109\n",
+      "Beta values for own Ridge implementation\n",
+      "[0.37396662 0.14174745 0.0764924  0.04892055 0.03447512 0.02586427\n",
+      " 0.02024962 0.01633913 0.01347916 0.0113104  0.0096208  0.00827728\n",
+      " 0.00719176 0.00630331 0.00556826 0.0049544  0.00443743 0.0039987\n",
+      " 0.0036237  0.003301  ]\n",
+      "Beta values for Scikit-Learn Ridge implementation\n",
+      "[0.37396662 0.14174745 0.0764924  0.04892055 0.03447512 0.02586427\n",
+      " 0.02024962 0.01633913 0.01347916 0.0113104  0.0096208  0.00827728\n",
+      " 0.00719176 0.00630331 0.00556826 0.0049544  0.00443743 0.0039987\n",
+      " 0.0036237  0.003301  ]\n",
+      "MSE values for own Ridge implementation\n",
+      "0.2640931530791004\n",
+      "MSE values for Scikit-Learn Ridge implementation\n",
+      "0.2640931530791002\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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pbN26NeJzp+ONfjckFrw352kuKPgxL7SdwpU3P1nn+/dluG+JHXPmzOHhhx+mtLSUU089lb/85S9+RxKRkNx3/0TjRnBJPXcvgQqEAFdccQVXXFF/91aLSM24QIC80kJGFJ/MCW3q/2kIDeIaRDx1o0nd0O+ExIL8N//Kl2ll5Jwx1pfjx32BSElJYceOHfqDIBWcc+zYsYOUlBS/o4gcVe6/n6ZRGVw64V5fjh/3XUwdO3akqKgIPUxIwqWkpBw2vIlItHGBAHkHVzKsrDUt21X94VQvxX2BSEpKOuonf0VEotGqJS+zqVkp/9PyEt8yxH2BEBGJRXlvP0WiwbjL/OleAhUIEZGo4wIB/rVvOReWtaT1KWf6liPuL1KLiMSate+/yqcnlJB9Ss3HQfOCCoSISJTJe/MJzMFlPnYvgbqYRESiTu6u9xkUaE7b02r/8LC6oBaEiEgUWZ8/jzXND5Ld/mK/o6hAiIhEk7z5jwIwYdw91azpPXUxiYhEkdzv3uVc0uh4Zj+/o6gFISISLTatXszKFgfIOelCv6MAKhAiIlEjb84jAGRfWv9De0eiLiYRkSiRt/3f9KUJnXtc4HcUQC0IEZGo8MXH7/NBi33ktBpU/cr1RAVCRCQKzHztNwBkj77T5yT/pS4mEZEokLf1bdItha59h/sdpYJaECIiPtv62Srea7GbnJbn+R3lMJ4WCDMbZWbrzWyjmR3xqQ8zu9rMCkNfS80so6bbiojEi1mvTMMZZI/4qd9RDuNZgTCzROApYDTQHbjSzLpXWu1zYIhzLh14CJhxDNuKiMSF3C/f4OxdyXQ/159nT1fFyxZEf2Cjc26Tc+4Q8BIwLnwF59xS59z3ocllQMeabisiEg+2f7GOfzffSXaz/n5HOYKXBaID8GXYdFFoXlV+CMw71m3NbLKZFZhZgZ47LSKx5pWZDxNIgJyLf+J3lCN4WSAswjwXcUWzoQQLRPnHB2u8rXNuhnMu0zmX2aZNm1oFFRHxS+7muZyxO4n0C3L8jnIELwtEEXBK2HRHYEvllcwsHfgzMM45t+NYthURiWXfbfmMt0/YQXZqHywh+m4q9TJRPtDVzLqYWTIwEZgdvoKZdQJmAtc65z49lm1FRGLd7JkPU5oIORf+2O8oEXn2QTnnXKmZTQHeABKBZ51za83sltDy6cD9QCvgD2YGUBrqLoq4rVdZRUT8kPvZa5yamEjfi67xO0pEnn6S2jk3F5hbad70sNc3ATfVdFsRkXixa9sXLEzbxpSyvlHZvQQaakNExBevz5zGoUaQc95kv6NUSQVCRMQHeetfoX1SAgNGTPI7SpWis10jIhLH9n73NfOabiU7oQcJidH7Pj16k4mIxKm5M39NcRLk9I94CTZqqECIiNSzvI/zOCnJOH/0zX5HOSp1MYmI1KMDu79jTuqXTOBsEpOS/Y5zVGpBiIjUozdmPcK+ZMjufb3fUaqlAiEiUo9yC1+iVZIxJGuK31GqpS4mEZF6cnDfbl5rvJnxZV1JSmnid5xqqQUhIlJP3nzld+xuDNk9r/Y7So2oQIiI1JPclc/TPAkuHn+H31FqRF1MIiL1oKR4P68mfcbYktNITk3zO06NqAUhIlIPFs1+nO9THDndJ/odpcZUIERE6kFuwd9IS4IRl93ld5QaUxeTiIjHSg8V84qtJ6u4EylpLfyOU2NqQYiIeOydOX9gexNHzik/8DvKMVELQkTEY7nLniO1BEbFUPcSqECIiHgqUFbKTPcxY/Z3oGnLk/yOc0zUxSQi4qGl82bwddMAOe0m+B3lmKkFISLiobz3/kzjUrgk+x6/oxwztSBERDwSKCslt7SQkcVtadaqvd9xjplaECIiHsl/868UpZWRfcalfkepFRUIERGP5C35I0llcOmEe/2OUivqYhIR8YALBMg9uJJhZW1o2a6L33FqRS0IEREPrFryMp83KyW7yxi/o9SaWhAiIh7Iffv3JBqMuyw2u5dABUJEpM65QIDcfflcWNaS1qec6XecWlMXk4hIHVv7/qt8ekIJOZ1G+R3luHhaIMxslJmtN7ONZnbEp0TM7Cwze9/MDprZ1ErLNpvZR2a2yswKvMwpIlKXchc+jjm47LL/8TvKcfGsi8nMEoGngOFAEZBvZrOdcx+HrfYdcBswvordDHXOfetVRhERL+TtXsagQHNO7tLD7yjHxcsWRH9go3Nuk3PuEPASMC58BefcNudcPlDiYQ4RkXrzyfK5rGl+kJwOw/yOcty8LBAdgC/DpotC82rKAQvMbIWZTa7TZCIiHsl741EAJoyLvbGXKvPyLiaLMM8dw/bnO+e2mNlJwEIz+8Q5t+SIgwSLx2SATp061S6piEgdyfvuPQa6NDp0y/Q7ynHzsgVRBJwSNt0R2FLTjZ1zW0LftwGzCHZZRVpvhnMu0zmX2aZNm+OIKyJyfDatXszKFgfIaXuR31HqhJcFIh/oamZdzCwZmAjMrsmGZtbUzJqVvwZGAGs8SyoiUgfy5jwCwISs2HpyXFU862JyzpWa2RTgDSAReNY5t9bMbgktn25mbYEC4AQgYGa3A92B1sAsMyvP+IJzbr5XWUVE6kLu9n+TSRM697jA7yh1wtNPUjvn5gJzK82bHvb6a4JdT5XtBjK8zCYiUpe++Ph9lrfYx8OJI/2OUmeO2sVkZteEvT6/0rIpXoUSEYk1M1/7DQDZo+/0OUndqe4axB1hr5+stGxSHWcREYlZuVvfImNnCl37Dvc7Sp2prkBYFa8jTYuINEhbNnzI0hZ7yG55nt9R6lR1BcJV8TrStIhIgzTr1Wk4g5yRP/M7Sp2q7iL1WWZWSLC1cHroNaHp0zxNJiISI3KLFtI9oTFnD8jyO0qdqq5AnF0vKUREYtS2zWtZ0nwn9zHI7yh17qgFwjn3n/BpM2sFDAa+cM6t8DKYiEgseOWVhwkkQM5Ft/kdpc5Vd5vr62bWI/S6HcFPM08C/h76UJuISIOWt3k+Z+xOouf5E/yOUuequ0jdxTlXPsTFjcBC59ylwAB0m6uINHA7ijbwVvMd5DTpiyXE3wM6qzuj8Oc0XEzoU9HOuT1AwKtQIiKxYPashylLgJwLb/U7iiequ0j9pZn9hODIrH2A+QBmlgokeZxNRCSq5W2aQ+fERvQZepXfUTxRXQvih8A5wA3AFc65naH55wLPeRdLRCS67dr2BQuabSO7cUZcdi9B9XcxbQNuiTB/EbDIq1AiItHu9ZnTKEmE7IHx+8DLoxYIMzvq8xucc2PrNo6ISGzIXf8KHRolMGBE/N6vU901iIEEnyv9IvABGn9JRIS9333N/KZbmVyWQUKip09N8FV1Z9YWGA5cCVwFzAFedM6t9TqYiEi0mjvz1xQnQXb/H/odxVNHvbLinCtzzs13zl1P8ML0RmBx6M4mEZEGKffjXE7en8D5o2/2O4qnqm0bmVlj4BKCrYjOwBPATG9jiYhEp/27vmVuahHXlnYnMSnZ7ziequ4i9V+BHsA84MGwT1WLiDRIb8x6hH3JkNPnBr+jeK66FsS1wD6gG3CbWcU1agOcc+4ED7OJiESd3I9eplWSMeTS+O9pr+5zEPH56Q8RkVo4uG83rzX+Dz8o6Uaj5BS/43gufu/PEhGpYwtn/ZY9jSEn/Vq/o9QLFQgRkRrKW/UizZPgonG3+x2lXqgLSUSkBg4d2MsrSZ8xruQ0klPT/I5TL9SCEBGpgUWzH2dniiOne3yO3BqJCoSISA3krfgHaUkw/LI7/Y5Sb9TFJCJSjdJDxcxKWM+lxaeSktbC7zj1Ri0IEZFqvDPnD3yb6sjudrnfUeqVCoSISDVylz1Hk0YwOvtuv6PUK0+7mMxslJmtN7ONZnZPhOVnmdn7ZnbQzKYey7YiIvUhUFbKTPcxYw50oEnz1n7HqVeeFQgzSwSeAkYD3YErzax7pdW+A24DfluLbUVEPLd03gy+bhog+6wJfkepd162IPoDG51zm5xzh4CXgHHhKzjntjnn8oGSY91WRKQ+5L73JxqXwiXZDa8jw8sC0YHg0+jKFYXm1em2ZjbZzArMrGD79u21CioiEkmgrJS80o8YubctzVq19ztOvfOyQER6PKmr622dczOcc5nOucw2bdrUOJyISHXy3/wrRWll5HRtmB0YXhaIIuCUsOmOwJZ62FZEpE7kLplOUhlcmn2v31F84WWByAe6mlkXM0sGJgKz62FbEZHj5gIB8g6uYtieNrQ4+VS/4/jCs89BOOdKzWwK8AaQCDzrnFtrZreElk83s7ZAAXACEDCz24Huzrndkbb1KquISGUrF7/I581K+T8tL/E7im88/aCcc24uMLfSvOlhr78m2H1Uo21FROpL3uI/kGgwbsL/+B3FN/oktYhIJS4Q4F/78hladiKtOnb1O45vNFifiEgla5bOYsMJJeScOsrvKL5SC0JEpJK8N5/EgPHjG+bdS+VUIEREKsndvYzBgeac3KWH31F8pS4mEZEwnyyfy9rmB8npMNzvKL5TgRARCZP3xqMAXDauYQ3tHYm6mEREwuR+9x7nuWZ06JbpdxTfqQUhIhLy2aq3WdXiANlth/odJSqoQIiIhOTNCT6aJvvSn/ucJDqoi0lEJCT32yX0oymnnnO+31GigloQIiLAf9a+R36LfWS3HuR3lKihAiEiAsx87REAssdM9TlJ9FAXk4gIkPf122RYCmf0vtjvKFFDLQgRafC++rSA91ruIaelrj2EU4EQkQZv1qu/BiBn1B0+J4ku6mISkQYv76s36Z7QmLP6j/E7SlRRC0JEGrRtm9eypPlOck4Y4HeUqKMWhIg0aK+88jCBBMi+6Cd+R4k6KhAi0qDlbp5H18Qkep4/we8oUUddTCLSYO0o2sDbzb8jp0kmlqA/h5WpBSEiDdbsWQ9TlgDZg3/sd5SopAIhIg1W7qbX6ZzYiD5Dr/I7SlRSm0pEGqRd275gYbPtZDfOUPdSFdSCEJEG6bW8hylJhJzzbvE7StRSgRCRBin301fo2CiR/sNv8DtK1FK7SkQanD07tjA/7WsmNOpBQqLeJ1dFPxkRaXDmzvw1BxtBTv+b/I4S1VQgRKTByVs3k5OTEjhv1GS/o0Q1T7uYzGyUma03s41mdk+E5WZmT4SWF5pZn7Blm83sIzNbZWYFXuYUkYZj/65vmZNaxATOJjEp2e84Uc2zFoSZJQJPAcOBIiDfzGY75z4OW2000DX0NQB4OvS93FDn3LdeZRSRhmf+zN+wPxly+t7od5So52ULoj+w0Tm3yTl3CHgJGFdpnXHA31zQMqCFmbXzMJOINHB5a/5JqwPG4Kxb/Y4S9bwsEB2AL8Omi0LzarqOAxaY2Qozq7Kj0Mwmm1mBmRVs3769DmKLSLw6uG83rzX+D5eVdaNRcorfcaKelwXCIsxzx7DO+c65PgS7oW41s8GRDuKcm+Gcy3TOZbZp06b2aUUk7i2c9Vv2NIbsvtf4HSUmeFkgioBTwqY7Altquo5zrvz7NmAWwS4rEZFay131Ai2KjYvG3e53lJjgZYHIB7qaWRczSwYmArMrrTMbuC50N9O5wC7n3FYza2pmzQDMrCkwAljjYVYRiXOHDuzl1aRNjCs5jeTUNL/jxATP7mJyzpWa2RTgDSAReNY5t9bMbgktnw7MBcYAG4H9QPltBScDs8ysPOMLzrn5XmUVkfi3aPbj7ExxZHe/0u8oMcPTD8o55+YSLALh86aHvXbAEbcSOOc2ARleZhORhiW34O80S4Lhl93pd5SYobGYRCTulR4q5pXET8k6eCopaS38jhMzNNSGiMS9Ja8/xbepjpwzf+B3lJiiAiEicS/3g+do0ghGTfi531FiirqYRCSulZUcYpZbx5gDHWjSvLXfcWKKWhAiEteWzp/B100D5LTP9jtKzFGBEJG4lrf0GVISYcyEu/2OEnNUIEQkbgXKSskr/YiRxW1p1qq933Fijq5BiEjcWr7wLxSllZHTbbzfUWKSCoSIxK28d/5IUhlkTTjieWVSA+piEpG45AIBcg+uYnhZG1qcfKrfcWKSWhAiEpdWLn6Rzc1KyTkty+8oMUsFQkTiUu6ip0gMwNjL7vU7SsxSF5OIxB0XCJC7v4CLyk6kVceufseJWWpBiEjcWbN0FhtOKCH71FF+R4lpakGISNzJffMJEhyMH6/upeOhAiEicSdv9wcMDrTg5C49/I4S09TFJCJxZd0Hr7O2+UGyOwzzO0rMU4EQkbiS98ZjAExQ99JxUxeTiMSVvO+Xcp5rRvuuffyOEvPUghCRuLFx5VusanGAnLYX+R0lLqgFISIxafsX6yjMf53VG96l8Nu1rC7bwsdpB7BEmHDpXX7HiwsqECIS1UqK97N+xRus/mghhUUrWL3vMwqTvmdr00DFOu0SEkgva8kI15PRva7k1HPO9zFx/FCBEJGosW3zWgoL5rB6wzsU7vg42CpoVkxJYnB5skF3UhkR6EJ643PI6HoB6f2yaNPpbH+DxykVCBGpd4cO7OWTgvkUrnkr1CrYRGHy93wd1ipon5BAetmJjApkkN6+LxnpI+jWZzhJKU18TN6wqECIiKe++XwNhQWvs3rjuxTuWMfqsi2sC2sVNE6AcyyVUYHTgq2CboNI75dF61PO9De4qECISN04dGAv6/LnUrjmbVZ/VUDhvs8pbLyTb5r8t1XQwRJIpxVjXC/S22aSkTGcbn1G0Cg5xcfkUhUVCBE5Zl9vKgxeKyhvFbitrEsrprRSq2BM4HTSU4Ktgp6Zl6hVEGNUIESkSgf37WZdwTwK17zF6q8+pHD/5xQ2/p5tTVzFOh0TEkkvPZEs15v0dplkZIyga+9hahXEARUIEcEFAnz9eXmr4D0Kvwu2Cj5JO1jRKkhJgHOsCVmBM0hP7UFGt8H0zLxEz1uIY54WCDMbBTwOJAJ/ds5Nq7TcQsvHAPuBG5xzH9ZkWxGpnYP7dvNx/hwK175d0SpYnbKTb1P/2yo4JSGR9NJWjOUM0ttnkp4+XK2CBsizAmFmicBTwHCgCMg3s9nOuY/DVhsNdA19DQCeBgbUcFuRuOQCAZwL1Py7Cxy+nXMVr/ft+pa1qxey+rPyVsHXfNLsIGWhQXZSEqCHNWFcWddgq+DMwfTsl8WJ7U/38Scg0cLLFkR/YKNzbhOAmb0EjAPC/8iPA/7mnHPAMjNrYWbtgM412LbO9P1ZEw5YmRe7jlqu+lWObX9WxzsEXB2ndBVfDmeHTwNHzDts2kLzKq9n//1ZHrZNhHWO9p2w717pFGoVjHNnkNGhf0WrIDEp2dsDS8zyskB0AL4Mmy4i2Eqobp0ONdwWADObDEwG6NSpU62Cnp1wModcaa22jWVGHf5FctTl3ioEeyHrcH+hszYXfGUWmsZC5xA2L3yd8GVVzCd8m/L/LMK8Kv7DETq2Hdv38mNjmCUcNr9xoxS6nzGQnv2yaNmuS53+LCX+eVkgIv3LrvyWsKp1arJtcKZzM4AZAJmZmbV6y/mP//d5bTYTEYlrXhaIIuCUsOmOwJYarpNcg21FRMRDXj4PIh/oamZdzCwZmAjMrrTObOA6CzoX2OWc21rDbUVExEOetSCcc6VmNgV4g+Ctqs8659aa2S2h5dOBuQRvcd1I8DbXG4+2rVdZRUTkSBa8gSg+ZGZmuoKCAr9jiIjEDDNb4ZzLjLRMjxwVEZGIVCBERCQiFQgREYlIBUJERCKKq4vUZrYd+E8tN28NfFuHcfwUL+cSL+cBOpdoFC/nAcd3Lqc659pEWhBXBeJ4mFlBVVfyY028nEu8nAfoXKJRvJwHeHcu6mISEZGIVCBERCQiFYj/muF3gDoUL+cSL+cBOpdoFC/nAR6di65BiIhIRGpBiIhIRCoQIiISkQpEBGY21cycmbX2O0ttmNlDZlZoZqvMbIGZtfc7U22Z2SNm9knofGaZWQu/M9WWmV1uZmvNLGBmMXd7pZmNMrP1ZrbRzO7xO09tmdmzZrbNzNb4neV4mdkpZrbIzNaFfrd+Wpf7V4GoxMxOAYYDX/id5Tg84pxLd871Al4H7vc5z/FYCPRwzqUDnwL3+pzneKwBJgBL/A5yrMwsEXgKGA10B640s+7+pqq1vwCj/A5RR0qBO51zZwPnArfW5f8XFYgjPQr8nCoecRoLnHO7wyabEtvnssC5igeGLyP4dMGY5Jxb55xb73eOWuoPbHTObXLOHQJeAsb5nKlWnHNLgO/8zlEXnHNbnXMfhl7vAdYBHepq/14+cjTmmNlY4Cvn3OryB8HHKjP7FXAdsAsY6nOcujIJeNnvEA1UB+DLsOkiYIBPWSQCM+sM9AY+qKt9NrgCYWZvAm0jLLoP+B9gRP0mqp2jnYdz7lXn3H3AfWZ2LzAF+EW9BjwG1Z1LaJ37CDann6/PbMeqJucSoyK9Y4rZlmm8MbM0IA+4vVIPwnFpcAXCOTcs0nwz6wl0AcpbDx2BD82sv3Pu63qMWCNVnUcELwBziOICUd25mNn1QBZwsYvyD+4cw/+XWFMEnBI23RHY4lMWCWNmSQSLw/POuZl1ue8GVyCq4pz7CDipfNrMNgOZzrmYG+3RzLo65zaEJscCn/iZ53iY2SjgbmCIc26/33kasHygq5l1Ab4CJgJX+RtJLPhu9hlgnXPud3W9f12kjk/TzGyNmRUS7DKr01vf6tnvgWbAwtBtu9P9DlRbZnaZmRUBA4E5ZvaG35lqKnSjwBTgDYIXQv/pnFvrb6raMbMXgfeBM82syMx+6Hem43A+cC1wUejfxyozG1NXO9dQGyIiEpFaECIiEpEKhIiIRKQCISIiEalAiIhIRCoQIiISkQqExCUz23sc204JjVh62Ii+FvREaFmhmfUJW5ZqZv82s0Qz61xXI4Wa2V/MLKcG6y2uboRYM3vJzLrWRS5pGFQgRI70HjAM+E+l+aOBrqGvycDTYcsmATOdc2X1krB2niY4EKVIjahASFwLvet/JPTBwY/M7IrQ/AQz+0NoDP3XzWxu+Tt159xK59zmCLsbB/zNBS0DWphZu9Cyq4EjxloKtSbeMbMPQ1/nheZfGGpx/NPMPjWzaWZ2tZktD+U8PWw3w0L7+NTMskLbp4ZaBIVm9jKQGnbMp82sIHRuD4bt553QvjSCgtSIflEk3k0AegEZQGsg38yWEPwEamegJ8EhVtYBz1azr0gjmnYwsx3AaVUUlW3AcOdccah750WgvCsoAzib4NDTm4A/O+f6hx768hPg9tB6nYEhwOnAIjM7A/gRsN85l25m6cCHYce8zzn3XegZDm+ZWbpzrtA5FzCzjaHjrqjmXEXUgpC4dwHwonOuzDn3DfBvoF9o/r+cc4HQYIyLarCvqkY0bQ3srGKbJOBPZvYR8C+CD9splx8az/8g8BmwIDT/I4JFodw/Qzk3ECwkZwGDgX8AOOcKgcKw9X9gZh8CK4FzKh1zGxCzTxiU+qUWhMS7qh7sUZsHflQ1omkxkFLFNj8DviH4rj0htG65g2GvA2HTAQ7/t1l5PBxXxXxCg+lNBfo55743s79UypYCHKgiq8hh1IKQeLcEuCJ0d1Ebgu+8lwPvAtmhaxEnAxfWYF+zgetC1zXOBXaFWgDfA4lmFqlINAe2OucCBAdVS6zFOVweynk6cBqwPnReVwOYWQ8gPbTuCcA+YFfovEZX2lc3ICYH2ZP6pwIh8W4Wwe6X1cDbwM9DXUp5BFsEa4A/EnwK1y4AM7stNOpqR6DQzP4c2tdcgl08G4E/AT8OO84Cgt1Wlf0BuN7MlhH847yvFuewnmDX2DzgFudcMcE7ktJCI/b+nGDRwzm3mmDX0lqC11TeK99JqGAccM5trUUGaYA0mqs0WGaW5pzba2atCP6BPb+2D4cys97AHc65a+s0ZB0ys58Bu51zz/idRWKDrkFIQ/a6mbUAkoGHjufJgc65lWa2yMwSo/izEDuBv/sdQmKHWhAiIhKRrkGIiEhEKhAiIhKRCoSIiESkAiEiIhGpQIiISET/H1GSr6ffdQ3uAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -924,12 +1078,138 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Beta values for own Ridge implementation\n",
+      "[ 3.43579948e-02 -5.43330971e-01 -3.10141413e-03  2.47116868e-01\n",
+      "  2.18613217e-01  1.02054837e-01 -4.25617655e-04 -5.90475506e-02\n",
+      " -7.68534263e-02 -6.68929213e-02 -4.24906604e-02 -1.40927184e-02\n",
+      "  1.11482289e-02  2.88529063e-02  3.67047975e-02  3.38135733e-02\n",
+      "  2.02198702e-02 -3.46383925e-03 -3.63025821e-02]\n",
+      "Beta values for Scikit-Learn Ridge implementation\n",
+      "[ 3.43579948e-02 -5.43330971e-01 -3.10141413e-03  2.47116868e-01\n",
+      "  2.18613217e-01  1.02054837e-01 -4.25617648e-04 -5.90475506e-02\n",
+      " -7.68534263e-02 -6.68929213e-02 -4.24906604e-02 -1.40927184e-02\n",
+      "  1.11482289e-02  2.88529063e-02  3.67047975e-02  3.38135733e-02\n",
+      "  2.02198702e-02 -3.46383925e-03 -3.63025821e-02]\n",
+      "Intercept from own implementation:\n",
+      "1.0330308045187284\n",
+      "Intercept from Scikit-Learn Ridge implementation\n",
+      "1.0330308045183763\n",
+      "MSE values for own Ridge implementation\n",
+      "3.1392559589438813e-06\n",
+      "MSE values for Scikit-Learn Ridge implementation\n",
+      "3.1392559585649594e-06\n",
+      "Beta values for own Ridge implementation\n",
+      "[-0.05807125 -0.29822833 -0.08551306  0.08156108  0.13679863  0.12333649\n",
+      "  0.08251519  0.03815288  0.00111756 -0.02498832 -0.04010697 -0.04566964\n",
+      " -0.04355837 -0.03562355 -0.02348765 -0.00848904  0.00831018  0.0260906\n",
+      "  0.04423486]\n",
+      "Beta values for Scikit-Learn Ridge implementation\n",
+      "[-0.05807125 -0.29822833 -0.08551306  0.08156108  0.13679863  0.12333649\n",
+      "  0.08251519  0.03815288  0.00111756 -0.02498832 -0.04010697 -0.04566964\n",
+      " -0.04355837 -0.03562355 -0.02348765 -0.00848904  0.00831018  0.0260906\n",
+      "  0.04423486]\n",
+      "Intercept from own implementation:\n",
+      "1.041148729430573\n",
+      "Intercept from Scikit-Learn Ridge implementation\n",
+      "1.041148729430526\n",
+      "MSE values for own Ridge implementation\n",
+      "1.9601304850211e-05\n",
+      "MSE values for Scikit-Learn Ridge implementation\n",
+      "1.960130485008338e-05\n",
+      "Beta values for own Ridge implementation\n",
+      "[-0.1416398  -0.14021063 -0.05383795  0.01367553  0.04784395  0.05796251\n",
+      "  0.05447415  0.044613    0.03267527  0.02098261  0.01066519  0.00217499\n",
+      " -0.00440346 -0.00917248 -0.01231917 -0.01405935 -0.0146081  -0.01416528\n",
+      " -0.01290947]\n",
+      "Beta values for Scikit-Learn Ridge implementation\n",
+      "[-0.1416398  -0.14021063 -0.05383795  0.01367553  0.04784395  0.05796251\n",
+      "  0.05447415  0.044613    0.03267527  0.02098261  0.01066519  0.00217499\n",
+      " -0.00440346 -0.00917248 -0.01231917 -0.01405935 -0.0146081  -0.01416528\n",
+      " -0.01290947]\n",
+      "Intercept from own implementation:\n",
+      "1.0495569966278246\n",
+      "Intercept from Scikit-Learn Ridge implementation\n",
+      "1.049556996627827\n",
+      "MSE values for own Ridge implementation\n",
+      "5.495916150935996e-05\n",
+      "MSE values for Scikit-Learn Ridge implementation\n",
+      "5.495916150936773e-05\n",
+      "Beta values for own Ridge implementation\n",
+      "[-0.13535942 -0.08593216 -0.03568439 -0.0036367   0.01397146  0.02229529\n",
+      "  0.02503753  0.0245528   0.02228115  0.01908936  0.01549377  0.01179792\n",
+      "  0.00817631  0.00472512  0.00149311 -0.00149956 -0.00424967 -0.00676387\n",
+      " -0.00905423]\n",
+      "Beta values for Scikit-Learn Ridge implementation\n",
+      "[-0.13535942 -0.08593216 -0.03568439 -0.0036367   0.01397146  0.02229529\n",
+      "  0.02503753  0.0245528   0.02228115  0.01908936  0.01549377  0.01179792\n",
+      "  0.00817631  0.00472512  0.00149311 -0.00149956 -0.00424967 -0.00676387\n",
+      " -0.00905423]\n",
+      "Intercept from own implementation:\n",
+      "1.0399676689527966\n",
+      "Intercept from Scikit-Learn Ridge implementation\n",
+      "1.0399676689527975\n",
+      "MSE values for own Ridge implementation\n",
+      "7.571105947979362e-05\n",
+      "MSE values for Scikit-Learn Ridge implementation\n",
+      "7.57110594797938e-05\n",
+      "Beta values for own Ridge implementation\n",
+      "[-0.05100875 -0.04063602 -0.02723445 -0.01713366 -0.0100706  -0.00517114\n",
+      " -0.00174276  0.00068734  0.00243186  0.00369758  0.00462287  0.0053018\n",
+      "  0.00579953  0.006162    0.00642221  0.00660427  0.00672607  0.0068011\n",
+      "  0.00683964]\n",
+      "Beta values for Scikit-Learn Ridge implementation\n",
+      "[-0.05100875 -0.04063602 -0.02723445 -0.01713366 -0.0100706  -0.00517114\n",
+      " -0.00174276  0.00068734  0.00243186  0.00369758  0.00462287  0.0053018\n",
+      "  0.00579953  0.006162    0.00642221  0.00660427  0.00672607  0.0068011\n",
+      "  0.00683964]\n",
+      "Intercept from own implementation:\n",
+      "0.999955585168597\n",
+      "Intercept from Scikit-Learn Ridge implementation\n",
+      "0.999955585168597\n",
+      "MSE values for own Ridge implementation\n",
+      "0.0007698473260556343\n",
+      "MSE values for Scikit-Learn Ridge implementation\n",
+      "0.0007698473260556316\n",
+      "Beta values for own Ridge implementation\n",
+      "[-0.00834567 -0.00803064 -0.00673407 -0.00554552 -0.00458878 -0.0038335\n",
+      " -0.00323332 -0.00274989 -0.0023548  -0.00202756 -0.00175331 -0.00152117\n",
+      " -0.001323   -0.0011526  -0.00100519 -0.00087697 -0.00076495 -0.00066668\n",
+      " -0.00058016]\n",
+      "Beta values for Scikit-Learn Ridge implementation\n",
+      "[-0.00834567 -0.00803064 -0.00673407 -0.00554552 -0.00458878 -0.0038335\n",
+      " -0.00323332 -0.00274989 -0.0023548  -0.00202756 -0.00175331 -0.00152117\n",
+      " -0.001323   -0.0011526  -0.00100519 -0.00087697 -0.00076495 -0.00066668\n",
+      " -0.00058016]\n",
+      "Intercept from own implementation:\n",
+      "0.9637117593816477\n",
+      "Intercept from Scikit-Learn Ridge implementation\n",
+      "0.9637117593816477\n",
+      "MSE values for own Ridge implementation\n",
+      "0.0023813163025848865\n",
+      "MSE values for Scikit-Learn Ridge implementation\n",
+      "0.002381316302584885\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -1064,10 +1344,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1180,10 +1457,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "X = np.zeros((n, L ** 2))\n",
@@ -1247,10 +1521,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "X_train_own = np.concatenate(\n",
@@ -1266,10 +1537,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def ols_inv(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n",
@@ -1354,10 +1622,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def ols_svd(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n",
@@ -1368,10 +1633,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "beta = ols_svd(X_train_own,y_train)"
@@ -1387,10 +1649,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "J = beta[1:].reshape(L, L)"
@@ -1406,10 +1665,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fig = plt.figure(figsize=(20, 14))\n",
@@ -1475,10 +1731,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1584,10 +1837,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "X = np.zeros((n, L ** 2))\n",
@@ -1617,10 +1867,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "clf = skl.LinearRegression().fit(X_train, y_train)"
@@ -1636,10 +1883,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "J_sk = clf.coef_.reshape(L, L)"
@@ -1655,10 +1899,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fig = plt.figure(figsize=(20, 14))\n",
@@ -1713,10 +1954,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "_lambda = 0.1\n",
@@ -1767,10 +2005,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "clf_lasso = skl.Lasso(alpha=_lambda).fit(X_train, y_train)\n",
@@ -1804,10 +2039,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "lambdas = np.logspace(-4, 5, 10)\n",
@@ -1870,10 +2102,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fig = plt.figure(figsize=(20, 14))\n",
@@ -2063,12 +2292,150 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Agegroup</th>\n",
+       "      <th>CHD</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>21</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>25</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>21</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>96</td>\n",
+       "      <td>61</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>97</td>\n",
+       "      <td>69</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>98</td>\n",
+       "      <td>65</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>99</td>\n",
+       "      <td>64</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>100</td>\n",
+       "      <td>63</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     ID  Age  Agegroup  CHD\n",
+       "0     1   21         1    0\n",
+       "1     2   23         1    0\n",
+       "2     3   25         1    1\n",
+       "3     4   29         1    0\n",
+       "4     5   21         1    0\n",
+       "..  ...  ...       ...  ...\n",
+       "95   96   61         8    1\n",
+       "96   97   69         8    1\n",
+       "97   98   65         8    1\n",
+       "98   99   64         8    1\n",
+       "99  100   63         8    0\n",
+       "\n",
+       "[100 rows x 4 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Common imports\n",
     "import os\n",
@@ -2137,12 +2504,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nAABXrlxB7dq1n3vfaCIiIsqfxQr91KlT2LVrF+Lj47Fs2TJkZmYiLCwMP/74IwCgf//+iI2NxbJly7B27VrMmjXLUlGIiIhkzyrvFGft0yeAfKaBrH0MgDzGIYcxABxHSSKHMQDyGEeJnHInIiIiy2ChExERyQALnYiISAZY6ERERDLAQiciIpIBFjoREZEMsNCJiIhkgIVOREQkAyx0IiIiGWChExERyQALnYiISAZY6ERERDLAQiciIpIBFjoREZEMsNCJiIhkgIVOREQkAyx0IiIiGWChExERyQALnYiISAZY6ERERDLAQiciIpIBFjoREZEMsNCJiIhkgIVOREQkAyx0IiIiGWChExERyQALnYiISAZY6ERERDLAQiciIpIBFjoREZEMsNCJiIhkgIVOREQkAyx0IiIiGWChExERyQALnYiISAZY6ERERDLAQiciIpIBFjoREZEMsNCJiIhkgIVOREQkAyx0IiIiGWChExERyQALnYiISAZY6ERERDLAQiciIpIBFjoREZEMsNCJiIhkgIVOREQkAyx0IiIiGWChExERyQALnYiISAbsLLnyiIgI7N+/H56enlAoFAgJCcn1+u3btzF//nzUr18fly5dQpcuXdCuXTtLRiIiIpIlixV6RkYGQkNDsXfvXiiVSowaNQrHjx+Hn5+faZnVq1ejcePGGDBgAP766y98+umnLHQiIqIXYLEp96ioKFSsWBFKpRIA4Ovri/Dw8FzLlC1bFomJiQCAxMRE1K1b11JxiIioNNHrYXvxApCcLHWSYmOxPfSEhAS4uLiYHqtUKiQkJORaZuDAgRg5ciTmzJmD6OhojBgxwlJxiIhIroSA7Y1rsDsbCbtzkbA/Gwm7i9FQ6HRAQACwcJnUCYuFxQrd09MTaWlppsdarRaenp65lpk4cSJ69uyJLl26IDExEe+88w4OHjwItVr93HV7eblaInKxk8M45DAGQB7jkMMYAI6jJCmxY4iLA06d+t//Tp8GkpL+97qdHdCwIdCkCRAUVHLHUcQsVug+Pj6IjY2FXq+HUqnE2bNn0a9fP2g0GtjZ2UGlUuHevXvw8vICALi5ucHGxgZGo9HsuuPjUy0Vu9h4ebla/TjkMAZAHuOQwxgAjqMkKTFj0GphHx0Fu7ORsD+Xswdue+d2rkUM1arD0LYDDL6NkdWoMQx16wNOTgBK0DheQkH/ILFYoTs5OWHGjBmYOXMmPDw84O3tDT8/P8yfPx9qtRpBQUGYNGkSvv/+e5w7dw537tzB6NGjUaZMGUtFIiKikiwrC3aX//rf1Pm5SNheuQzFEzt6xrJe0L37HgyNHpW3TyMID/YGACiEEELqEIVl7X9tAfL5q9HaxwDIYxxyGAPAcZQkFh+DELCJuWHa67Y/Gwm7C+ehyMz83yLOLsjyaZRT3r6NYfDxhbHSa4BCUeC3kctnURAWvQ6diIgIABTx8bCPisw1dW7zxHFvYWsLwxt1YWjUGIbGbyKrUWNk1/IGbG0lTG1dWOhERFS00tJgf+F87qnzWzdzLZJdpSoyW7d9NHX+Jgz1GwDOzhIFlgcWOhERvTiDAbaX/srZ6446C/uzkbC9/Ffu496entC1fydn79u3MbJ8GkM8ddUTvTwWOhERFYwQsLl1M6e8z0bC/uyZnOPeGRn/W8TJCYa3muacsPborHNj5SqFOu5NL4aFTkREeVIkJJiOez+eOrd54gZhwsYG2bXr5Jyw9uis8+zab+RcB07FjludiIhycfx+LbBsMcpev57r+ezKVZDZotX/ps7rNwSeuCMoSYuFTkREOQwGuEyfBOfVKwFXV+jbtv/f1LlPY4hHNwKjkomFTkREUKQkwy1oIJSHDsLwRh3Y/bIXyS48cc2aWOzb1oiIyDrY3IyBuss7UB46CF27DtD8vB+oWlXqWFRILHQiolLM7tRJeLzXFnaXLyF9aDBSNmyGcHWTOha9AE65ExGVUg5bN8P105FAdjZS5y1E5sAhUkeil8BCJyIqbYxGOM+fDZeF82F0dUPK6vXIatNO6lT0kljoRESlSUYGXD8eDsdd25FdpSqSf9iCbO/aUqeiIsBCJyIqJRRxcXD/qA/sz0Yiq6kfktf9yFuwyghPiiMiKgVsL16AR8c2sD8bicxefaHZuptlLjMsdCIimVP+9is8urwD27t3oJ0SitSlKwAHB6ljURFjoRMRyZUQcFr+Ddz69wGEEclrNiDjk7H8ohSZ4jF0IiI5ysqCauJYOG1Yh+xy5ZGy4ScYfHylTkUWxEInIpIZhSYJboP7Q3n0CLLqNUDKD5thrPiq1LHIwjjlTkQkI7bX/4H6vXZQHj0CXcfO0OzexzIvJVjoREQyYX/sKNQd28Lu2j9ID/kUKes2AiqV1LGomHDKnYhIBhx/3ADVZ58AAFIXfYvMfoESJ6LixkInIrJmRiNc/hMK528Xw+jhgZTvfkBW85ZSpyIJsNCJiKyVVgu3EUPhsG8vDNVrIGXjFmRXqyF1KpIIC52IyArZxN6FW0Bv2F+Mhr5lK6Ss+R5C7SF1LJIQT4ojIrIydlFnoX63DewvRiMjcACSf9rOMifuoRMRWRPlnl1wCwkCMjOh/WI2MoaN5J3fCAALnYjIOggBpyULoZr1OYSzC1K+/wn6d9+TOhWVICx0IqKSTqeD69iP4bhlE7JfrYTkDZuRXa++1KmohGGhExGVYIqEBLgP6Af7k8eR5dsYyet/gihXTupYVALxpDgiohLK9u8rOd9hfvI4Mrv2gGbHLyxzyhcLnYioBLI//DvUndrD9mYM0saMR+rK7wAnJ6ljUQnGKXciohLGce1qqCaPA2xtkbJsFXQf9pY6ElkBFjoRUUlhMMAldDKcV62AsWxZJK/bBEOTplKnIivBQiciKgEUqSlwDRoIh98PwOBdG8k/bIGxSlWpY5EVYaETEUnM5tZNuAf0gt3lS9C3bY+UsLUQbu5SxyIrw5PiiIgkZHf6JDw6toHd5UtIHzIMyT9sYZnTCzFb6BEREThw4AAAYPXq1Rg1ahQuXbpk8WBERHLnsG0L1D26QJGUhNQ5XyJt9gLAjhOn9GLMFvqWLVtQq1YtREdHY8uWLejWrRtWrlxZHNmIiORJCDjPmwW34UMglA5I3vhfZA4OkjoVWTmzhV6lShVUqVIFv/76K/r374927dqhYsWKxZGNiEh+MjLgOmwgXL6ah+zKVaH55SCy2raXOhXJgNm5nVu3bmHfvn34+eefsWvXLhiNRsTFxRVHNiIiWVHExcF9QF/YR55BVpO3kbzuR4iyZaWORTJhdg89MDAQu3btwscff4wyZcpgwYIFqFGjRnFkIyKSDds/L+bcxjXyDDJ79oFm2x6WORUps3vovr6+WL58uenxhAkTLBqIiEhulAf2wTVoEGzStEibPB3pn4zld5hTkTO7hx4TE4OAgAD4+/sjPT0dwcHBuHPnTnFkIyKybkLAacU3cAvsA0W2Aclrvkf6p5+xzMkizBb60qVLMXLkSFSuXBnOzs6YOXMmli1bVhzZiIisV1YWVJ99CtX0yTCW9YJm16/Qv99N6lQkY2YLvVKlSvDz84NSqQQAlC1bFu7uvOkBEVG+kpLg3uffcNqwFln1GkDz22EYGjWWOhXJnNlCf/DgATIzM6F4NEUUGxuLmJgYS+ciIrJKNtevAX5+UB4Nh65jJ2h274Px1UpSx6JSwOxJcd26dUOnTp2g0+lw+vRpJCQkYNGiRcUQjYjIuthFnYV77+5AUhLSR3yMtGmfA7a2UseiUsJsoTdt2hTbt29HVFQUhBBo1KgR1Gp1MUQjIrIedpGn4d67BxTaVCAsDGnd+kgdiUqZAn05i1qtRuvWrdGmTRuo1Wr897//tXQuIiKrYXfqJNx7doNCm4rUb8OAoUOljkSlkNk99P79+z/z3M2bN9GzZ0+LBCIisib2JyLg1vdDKDIzkBK2FvoPuksdiUops4Xu5uZmKnWDwYBLly5Bp9NZPBgRUUlnf+wo3P17Ano9Ulath77LB1JHolLMbKEvWLAATk5OpsfNmjXDvHnzCrTyiIgI7N+/H56enlAoFAgJCcn1uhACGzZsAADcvXsXKSkpmDNnTmHyExFJwv7IYbj37wMYDEhZswH69zpLHYlKObOFfvHiRdO/jUYj4uPjce7cObMrzsjIQGhoKPbu3QulUolRo0bh+PHj8PPzMy2za9cuuLm5oVu3bgCAy5cvv8AQiIiKl/2hg3Af0A8wGpGybiP0HTpKHYnIfKGPHTsWVatWhRACCoUCXl5emDhxotkVR0VFoWLFiqYb0vj6+iI8PDxXoe/ZswctW7bE999/j4cPH/K4PBGVeMqDv8FtgD+gUCD5+03IattB6khEAApQ6GPGjDHtQRdGQkICXFxcTI9VKhUSEhJyLRMbGwutVouQkBDcuHEDQ4YMwS+//AJbM9dtenm5FjpPSSSHcchhDIA8xiGHMQAlfBx79gAf9cu5tnzPHqjb5/895iV6HAUkhzEA8hmHOQW6sczTVq1ahaFmLsvw9PREWlqa6bFWq4Wnp2euZVQqFRo2bAgAeP3116HVanHv3j1UqvT8uyrFx6eai13ieXm5Wv045DAGQB7jkMMYgJI9DuXePXAb+hGgVCL5hy3IatgUyCdrSR5HQclhDIA8xlHQP0jyLfS2bduabvf6JCEEUlJSzBa6j48PYmNjodfroVQqcfbsWfTr1w8ajQZ2dnZQqVTw8/PD7du3AeQUfnZ2Nry8vAoUnIiouCh374DbsEGAgyOSN21Fll9zqSMRPSPfQvf19cXo0aOfeV4IgW+++cbsip2cnDBjxgzMnDkTHh4e8Pb2hp+fH+bPnw+1Wo2goCAMHToUCxYswIoVK3Dr1i3MmzcPDg4OLzciIqIi5LBjK1xHDIVwckbypm0wNH1b6khEeVIIIUReL2RkZOS6XO1JV65cgbe3t0WDPY+1T58A8pkGsvYxAPIYhxzGAJS8cTj89ye4jgqGcFEhefN2GN5sUqCfK2njeBFyGAMgj3G89JT7k2UeHR2NmJgYGI1GAMDu3bvx3XffvWREIqKSy+GnjXD9ZASEmzuSt+zg159SiWf2pLilS5fi4sWLuHv3LurXr4/Y2Fikplr3XztERM/j+MN6qMZ+DKFWI/m/u2Bo4CN1JCKzzH45S3JyMlauXIlmzZphzpw5WL9+PZo2bVoc2YiIip3jujVwHTMKwsMDmm0/s8zJapgt9Mc3hnnyErR79+5ZLhERkUQc16yE6/jRMJYtC832vciuV1/qSEQFZnbK/fr16zhw4ABq1qyJHj16QKVSwd7evjiyEREVG6cV30A1fTKMXq9As/1nZHvXljoSUaGYLfRvv/0WAGBrawsvLy9oNBp07drV4sGIiIqL0zeLofpiGrLLlUfy9p+RXbOW1JGICs3slPvUqVNN/+7cuTP8/f2hUqksGoqIqLg4Lf4qp8wrVETyrl9Y5mS1zO6h37p1C1OmTIGXlxe6d++OatWqFUcuIiKLc/5yLlzmz0Z2pdeg2bYHxtf53zeyXmYL/csvv0SFChVw//59bNu2DTdu3EDz5s3RvXv34shHRFT0hIDzvFlwWTgf2ZWrQLP9ZxgrV5E6FdFLMTvlrtFoAOTcaz0lJQXHjx/H3r17LZ2LiMgyhIDL7C9yyrxKVWh2/sIyJ1kwu4c+bdo02NjYICEhAT169MDWrVtRoUKF4shGRFS0hIDL59PgvGwJDNWqI3n7zzBWfFXqVERFwmyhG41GjBkzBs2aNSuOPEREliEEXKZPgvPKZTDUqJlT5uW5c0LyYbbQFyxYgOrVqxdHFiIiyxACqsnj4LQmDAbv2tBs3QNRrpzUqYiKlNlCZ5kTkVUzGqGaMBZO69fA8EadnDL38pI6FVGRM1voRERWy2iE6rNP4PTDehjq1odm624IT0+pUxFZBAudiOQpOxuuo0Pg+NNGZDXwQfJ/d0J4lJE6FZHFmL1sDQB0Oh3u37+P2NhYxMbGYtKkSZbORUT04gwGuI4KzinzRr5I3rqLZU6yZ3YPfdGiRVi/fj3UajUUCgUAICUlBXPmzLF4OCKiQjMY4DpyKBx3bENW47eQvHk7hJu71KmILM5soR86dAh//PEHXFxcTM/99NNPFg1FRPRCsrLgFjwYDnt2IqvJ20jetBXC1U3qVETFwmyh16lTBw4ODrmeq1KFd1UiohJGr4db0EA4/LIHer/mSN74X4BfJEWliNlCT0tLQ5cuXVC3bl0olUoAQHR0NG//SkQlh04HtyH94fDbr9C3+BeSN2wGnphVJCoNzBb6jRs3MGzYsFzP3b9/32KBiIgKJTMTboMC4HBwP/St2iB5/SbA2VnqVETFzmyh/+c//0GjRo1yPefj42OpPEREBZeRAfeP+kIZfgj6tu2RvHYj4OQkdSoiSZi9bO3pMgfA6XYikl56OtwDekMZfgi6Du8ied2PLHMq1czuoUdGRmLatGm4efMmjEYjhBBQKBQICQkpjnxERM/SauEe2BvKY0eh69gZKavWAU+dvEtU2pjdQ9+0aRN++OEH+Pv749KlSzh06BAGDx5cHNmIiJ6h0KZC3fffOWXe+QOkrF7PMidCAQq9YsWKKFOmDIxGo+lxZmamxYMRET1NkZoC917dYX/yODK79kBK2Frg0dU3RKVdgc5yv3//PrKzs7Fu3Tqo1WqcPXu2OLIREZkokjVw790d9mcjkdmjJ1K/WQnY8esoiB4z+9sQEBCABw8eIDg4GJMnT4ZGo8Fnn31WHNmIiAAAiqTEnD3z8+eQ2asvUhcvA2xtpY5FVKKYLfSmTZua/r1mzRqLhiEiepoiMQHuH3aF/cVoZPQLhParJSxzojyYPYYeExODgIAA+Pv7IyMjA8HBwbhz505xZCOiUk7x8CHU3bvklHngQGgXLmWZE+XDbKEvXboUI0eOROXKleHk5ISZM2di2bJlxZGNiEoxxYMHUPfoDLtLfyJj4BBoF3wN2BToG5+JSiWzvx2VKlWCn5+f6T7uZcuWhbs7v4qQiCzHJu4+1N07we7yJaQPDYZ27lcscyIzzP6GPHjwAJmZmabvQo+NjUVMTIylcxFRKWVzLxbu3TrB7urfSA8OQdrMecCj//4QUf7MnhTXrVs3dOrUCTqdDqdPn0ZCQgIWLVpUDNGIqNS5fRvqru/BNuYG0keNRtrUGSxzogIq0Fnu27dvR1RUFIQQaNSoEdRqdTFEI6LSxOb2LeDD92EbcwNpY8YhfcJUljlRIRTooJRarUbr1q3Rpk0bqNVqrF+/3tK5iKgUsT8RAfV77YAbN5A2bhLSJ05jmRMVktk99CNHjmDFihV4+PCh6ctZUlJS8NFHHxVHPiKSMyHgtPJbuHw+Lefx118j3Z/fFUH0IswW+ty5czFt2jRUrlwZCoUCQgh88803xZGNiGRMoU2FavQoOO7aDqPXK0hZtQ7qru8B8alSRyOySmYLvUaNGmjWrFmu50aMGGGxQEQkf7Z/X4HboADY/X0FWU3eRsrq9TCWryB1LCKrZrbQBw4ciNDQUNStW9d0Lfru3bvx3XffWTwcEcmPcvcOuH4yEjZpWqQPG4G06f8B7O2ljkVk9cwW+vLly5Geng6dTme6Fj0uLs7iwYhIZrKy4PLFdDiv/BbC2QUpYWuh6/ZvqVMRyYbZQtdqtdi0aVOu544cOWKxQEQkPzZx9+E6dACUJyJgqFETKWs3Itu7ttSxiGTF7GVrLVu2xK1bt3I99/RjIqL82J+IgLpdSyhPRED3fjdofjvMMieyALN76Fu3bsWyZcvg4eEBpVJpumwtMDCwOPIRkbV66pI07eezkRE8kteXE1mI2UIvX748NmzYYHrMy9aIyJxnLklbvR5Zfs2ljkUka2YLfc2aNXBycsr13Jw5cywWiIism+3fV+A20B92V//mJWlExcjsMfSnyxyA6Wx3IqInKXfvgPrdNjnflDZsBDQ79rLMiYqJ2T10IiKznr4kbdU66Lr2kDoVUanCQieil5LrkrSatZDy3Q88i51IAmYLPTY2FhcuXIBCoUC9evVQsWLF4shFRFbA/kQEXId8BNsHcdC93w2pi7+FULlKHYuoVHpuoc+aNQsbN26Es7MzhBDIyMiAv78/pkyZUlz5iKgkEgJOK76Fyxe8JI2opMj3pLjNmzfj2rVr2Lt3L86cOYPIyEj8/PPPuHbtGn766acCrTwiIgIzZszA0qVLn3up2+7du+Ht7Y20tLTCj4CIipVCmwrXoQOgCp0Mo2dZJG//GRnDQ1jmRBLLt9APHTqEJUuW4PXXXzc9V61aNSxZsgSHDx82u+KMjAyEhoZi8uTJGDVqFK5cuYLjx48/s9y1a9dw7dq1F4xPRMXJ9u8rUL/bBo67dyCrqR80vx/l9eVEJUS+ha5SqaBSqfJ8Xq1Wm11xVFQUKlasaPqGNl9fX4SHh+daJiMjA6tXr8bIkSMLl5qIil3uS9JGQrP9ZxjLlZc6FhE9ku8xdFfX/E9sed5rjyUkJMDFxcX0WKVSISEhIdcyX3/9NUaMGGEq/YLy8pLHSTdyGIccxgDIYxwWG0NWFjBhAvD114CLC7B5M5x79YKzZd5NFp8FII9xyGEMgHzGYU6+hb5z504cPHgwz9fS0tIwderU567Y09Mz1zFxrVYLT09P0+N79+4hJSUFv/76q+m5tWvXolWrVqhfv/5z1x0fn/rc162Bl5er1Y9DDmMA5DEOS43BJu4+3IZ8BPuTx3MuSVu7Edm1vAELbS85fBaAPMYhhzEA8hhHQf8gybfQ/fz8MHDgwGeeF0Lkurd7fnx8fBAbGwu9Xg+lUomzZ8+iX79+0Gg0sLOzQ4UKFTB37lzT8l999RUGDhyYa6+eiKTz5CVpmR90h3bRN7wkjagEy7fQx40bh2rVquX52iuvvGJ2xU5OTpgxYwZmzpwJDw8PeHt7w8/PD/Pnz4darUZQUBAAIDEx0XTW/OrVq9GnTx+UK1fuRcZCREWBl6QRWSWFEELk9cLhw4fRpk2bPH/oyJEjaNWqlUWDPY+1T58A8pkGsvYxAPIYR1GNQaFNherTEDju3oHsV8ohdfV6ZL3drAgSFowcPgtAHuOQwxgAeYzjpafcv/32W/z55595vvZ///d/khY6ERW9J78lTf92M6SuWsez2ImsSL6FnpKSguvXrwPIuQTNx8cn12tEJB8Ou7bD9ZORUKSnIX3YSKRN/wKwt5c6FhEVQr6FHhQUhA8//BAAMGbMGCxcuND02vbt2y2fjIgsLysLLl9Mg/PKZfyWNCIrl2+hPy5z4NnvP+/Rg7/wRNYu30vSiMgq5XunuOfdjvXxVDwRWSf7ExFQt2sJ+5PHkflBd2h+O8wyJ7Jy+e6hL1iwAIGBgRBCID4+Hn/88YfptR9//BHLli0rloBEVISeviTti9nIGMZL0ojkIN9CP3XqFP7++2/T4+nTp5v+zZPiiKyPQpsK109GwmHPTkkuSSMiy8q30AMCAjBmzJg8X1u8eLHFAhFR0eMlaUTyl+8x9PzKHAA++eQTi4QhoqLnsGs7PN5pnfMtacEhSN62h2VOJEP5Fnp0dDTmzJmDU6dOmZ67efOm6TatRFTCZWXBZdpEuA0dAKFQIHn1eqR9MZvXlxPJVL5T7t9//z2qVq2KOnXqmJ7z9PTE+fPnodPp8NFHHxVLQCIqvFyXpNXyRsp3P/AsdiKZy3cP3WAwICQkBCqVyvScSqXCnDlzcP78+WIJR0SFZ3/8GDzatsi5JK1rD2j2HWKZE5UC+Ra6m5tbvj/k4eFhkTBE9BKEgNOypXDv0QWKpERo/zMHqWFr+ZWnRKVEvlPuaWlp+f6QTqezSBgiekGpqXAb8pHpkrSU1d/D8Laf1KmIqBjlu4fu7e2Nr7/+Gnq93vScTqfDkiVL8MYbbxRLOCIyz/7YUaBJEzjs2Qn9282g+f0oy5yoFMq30IcOHQqNRoO3334bXbp0QZcuXdCsWTOkpKTA39+/ODMSUR7sjx2Fe7dOUHfvDFy+zEvSiEq5fKfcFQoFPv/8cwQFBeHChQsAgAYNGqBixYrFFo6InmV/7CicF8yBMiLndsy69u/AYdZ/kPY6Z86ISrN8C/2xV199Fa+++mpxZCGi58iryNM/mwiD75vw8nIF4lMlTkhEUjJb6EQkrecVORHRYyx0ohKKRU5EhcFCJyphWORE9CJY6EQlhP2xo3D+ci6Ux44CYJETUeGw0IkkxiInoqLAQieSiH3EHzlT6yxyIioCLHSiYvZMkbfrkFPkjd+SOBkRWTMWOlExYZETkSWx0IksjEVORMWBhU5kIfYRf+Sc7PbH/wFgkRORZbHQiYoYi5yIpMBCJyoi9seP5Uyts8iJSAIsdKKXxCInopKAhU70gp4ucn3b9kj7bCIMbzaROBkRlUYsdKJCYpETUUnEQicqIBY5EZVkLHQiM+yPH8s5a/3oEQAsciIqmVjoRPlgkRORNWGhEz3F/kREztQ6i5yIrAgLnegRFjkRWTMWOtHRo3CfMu1/Rd6mXU6Rv9VU4mBERAXHQqdSy+bObbiO/Rg4/DuUYJETkXVjoVOp5LBjK1TjRsMmJRlo3x5JoyewyInIqtlIHYCoOClSkuE6fAjchg2CwmBA6tffAPv3s8yJyOpxD51KDfvjx+A6Mgi2d24jy7cxUpetQna1GnBVKKSORkT00riHTvKn18Nl1udw79YJNrF3kTZ2AjR79iO7Wg2pkxERFRnuoZOs2V79G64jhsL+/DlkV6mKlGWrOL1ORLLEPXSSJyHguG4NPNq3hP35c8joG4Ckw8dY5kQkW9xDJ9lRxMfDdfRIOOzfB6NajZRvVkL/fjepYxERWRQLnWRFuf9XuH4aApuH8dD/qw1Sly6HsUJFqWMREVkcC53kIT0dqhlT4LRuDYRSCe0Xs5ERNAKw4VElIiodWOhk9ezOn4Pr8CGw++cqDG/UQcryNciuU1fqWERExYq7L2S9srPhtPgrqN9rB7t/riJ92Egk/RbOMieiUol76GSVbG7fguvIIChPRCC7fAWkLlmOrNZtpY5FRCQZixZ6REQE9u/fD09PTygUCoSEhOR6PSwsDA8fPkTZsmXx559/4uOPP0b16tUtGYlkwGHrZqgmjIVNagp0Xboi9ctFEGU8pY5FRCQpixV6RkYGQkNDsXfvXiiVSowaNQrHjx+Hn5+faZn09HRMmjQJCoUCv/zyCxYsWIAVK1ZYKhJZOUWyBqoJY+C4fSuMLiqkLFkOXe9+AG/dSkRkuWPoUVFRqFixIpRKJQDA19cX4eHhuZb59NNPoXj0H2Oj0QhnZ2dLxSErZ3/sKDxaN4Pj9q3IerMJkg79AV0ff5Y5EdEjFttDT0hIgIuLi+mxSqVCQkJCnsvq9Xrs2LEDoaGhBVq3l5drkWSUmhzGYfEx6PXAtGnAggU5l6B9/jnsJ0+Gp13R/r8uP4uSg+MoOeQwBkA+4zDHYoXu6emJtLQ002OtVgtPz2ePc+r1esyYMQOjR49G5cqVC7Tu+PjUIsspFS8vV6sfh6XHYPv3FbgGD4b9xWhkV3095z7sbzYBkjKK9H34WZQcHEfJIYcxAPIYR0H/ILHYlLuPjw9iY2Oh1+sBAGfPnkXr1q2h0Wig1WoBAJmZmQgNDcXAgQNRr149/Pbbb5aKQ9ZECDiuCcu5D/vFaGT490fioWM5ZU5ERHmy2B66k5MTZsyYgZkzZ8LDwwPe3t7w8/PD/PnzoVarERQUhM8++wxXr17FnTt3AOScJPfuu+9aKhJZAUVcHFw/HQGH3w/AWKYMUpavgb7z+1LHIiIq8RRCCCF1iMKy9ukTQD7TQEU5BuW+X+A6eiRsEhKgb90WqUuWw1i+QpGtPz/8LEoOjqPkkMMYAHmMo6BT7ryxDEkvLQ2q6ZPhtGEthIMDtLPmIWPwMN6HnYioEFjoJCm7c5E592G/fg2GOvWQsmINsmu/IXUsIiKrw10gkkZ2Npy/XgB15w6wu34N6SM+RtJvh1nmREQviHvoVOxsbsbAbWQQ7E+dQHaFikj9ZiWyWraSOhYRkVXjHjoVHyHgsPlHeLRpDvtTJ5D5QXckhUewzImIigD30KlYKJISoRo/Bo67tsOockXK0hXQ9erLW7cSERURFjpZnP3RI3ANGQbbe7HIeqspUpatgrFKValjERHJCqfcyXJ0OriEToH63+/D5kEc0iZOhWbXryxzIiIL4B46WYTt5UtwCx4Mu78uwlCtOlKXrYLB902pYxERyRb30KloGY1wWrUcHh3+Bbu/LiIjcACSDh5lmRMRWRj30KnI2MTdh+vHw6E8/DuMnp5ICVsH/XudpY5FRFQqsNCpSCj37oHr2FGwSUyEvm17pCxeDlGunNSxiIhKDRY6vRytFqppE+G08XsIR0ekzlmAzEFBvByNiKiYsdDpxZ08iTJ9+sI25gay6jVA6vLVyPauLXUqIqJSiYVOBSMEbO7Fwu58FOzOn4NddBRw+HfYGI1ID/kUaROmAA4OUqckIiq1WOj0LCFgE3s3p7yjz8HufBTsz0fB5mF87uW8vZE85ytktfiXNDmJiMiEhV7aCQGbO7cflXcU7KNz/q/Nw4e5Fsuu9Bp0nd6HoaEPshr6wFDfB2XrVENWfKpEwYmI6Eks9NJECNjcvpWzxx39aOr8wnnYJCTkWiy7chXoujR/VNwNYWjYCMLTU6LQRERUECx0uRICNjdjHu11nzcd97ZJSsq1WHblqtA1a5lT3g18YGjQEKIMy5uIyNqw0OVACNjE3Hi01/3ofxeiYKPR5Fosu0pVZLZsnVPcDR+Vt0cZaTITEVGRYqFbG6MRtjHXYRd93nTc2y76PGySNbkWM7xeDfrWbWGo/0R5qz2kyUxERBbHQi/JjEbY3rj2v73u6CjYXYiGTUpyrsUM1apD37YdDA0a5ZR3/QYQ7mppMhMRkSRY6CWF0Qjb69dyjnU/Wd6pKbkWM1SvAX37DrnL281dotBERFRSsNClkJ0NXLoEh8N/5BT3+UflnaY1LSIUCmTXqAn9Ox1zirthIxjq1YdwdZMwOBERlVQs9GKm0CTBo5UfcC8Wj6tZKBTIrlkL+scnqz0ub5WrpFmJiMh6sNCLmVA6wPBmE9iqXaH1rousBjnlDZVK6mhERGTFWOjFzdkZKWu+h5eXKzJ4lzUiIioiNlIHICIiopfHQiciIpIBFjoREZEMsNCJiIhkgIVOREQkAyx0IiIiGWChExERyQALnYiISAZY6ERERDLAQiciIpIBFjoREZEMsNCJiIhkgIVOREQkAyx0IiIiGWChExERyQALnYiISAZY6ERERDLAQiciIpIBFjoREZEMsNCJiIhkgIVOREQkAyx0IiIiGWChExERyQALnYiISAZY6ERERDJgZ8mVR0REYP/+/fD09IRCoUBISEiu13U6HebNm4dy5cohJiYGQUFBeP311y0ZiYiISJYstoeekZGB0NBQTJ48GaNGjcKVK1dw/PjxXMusX78eFSpUwLBhwzBgwABMmTLFUnGIiIhkzWKFHhUVhYoVK0KpVAIAfH19ER4enmuZ8PBwNGrUCADg7e2Ny5cvQ6vVWioSERGRbFlsyj0hIQEuLi6mxyqVCgkJCQVaRqVSPXfdXl6uRRtWInIYhxzGAMhjHHIYA8BxlCRyGAMgn3GYY7E9dE9PT6SlpZkea7VaeHp6FnoZIiIiMs9ihe7j44PY2Fjo9XoAwNmzZ9G6dWtoNBrTtHrr1q1x7tw5AMCVK1dQu3Zts3vnRERE9CyFEEJYauXHjh3Db7/9Bg8PD9jb2yMkJATz58+HWq1GUFAQMjMzMW/ePHh5eeHWrVsYNmwYz3InIiJ6ARYtdCIiIioevLEMERGRDLDQiYiIZMCid4oraubuPGcN4uPjsWjRIly+fBnbtm2TOs4LuXXrFhYtWoQ6derg/v37UKvVVvdZGI1GBAcHo0GDBsjKysLt27cxe/ZsODo6Sh3thWRmZqJnz55o0aIFJkyYIHWcF9KrVy84ODgAAGxsbLB+/XqJExXe9evXsXfvXjg4OOD06dMYNWoUGjRoIHWsQrlz5w4GDBiAChUqAMi5+sjb2xtz586VOFnhrF69Gnfv3oWHhwdu3ryJWbNmWeXv97p16xAXFwcnJyfo9XqMHTsWCoUi74WFlUhPTxft27cXOp1OCCFESEiIiIiIkDhV4f3666/i999/F927d5c6ygs7f/68OHDggOnxe++9Jy5cuCBhosLLzs4W3377relxcHCw2LVrl4SJXs6cOXPE+PHjxdy5c6WO8sKWLFkidYSXYjAYxNChQ0V2drYQQoi4uDiRkJAgcarCS0xMFMeOHTM9Xrx4sTh9+rSEiQrvwYMH4q233jJ9Ftb6+/3XX3+JDz74wPQ4JCRE7N+/P9/lrWbKvSB3nrMGHTt2zHUzHWvUoEEDtG/f3vTYaDTCyclJwkSFZ2NjgxEjRgAADAYD4uLirPYKi507d8LX1xeVKlWSOspL+fvvvxEWFoalS5da5e/2hQsXIITAhg0bsHLlShw+fBgeHh5Sxyo0Dw8PNGvWDACg1+tx8eJFvPnmmxKnKhwnJyfY29ubLpFOT09HzZo1JU5VeDExMaaZEgCoVKnSM7dQf5LVTLkX5M5zVPwOHDiAFi1aoHr16lJHeSFHjx7FunXr0Lp1a9SvX1/qOIX2zz//4Pr16xgzZgyuXLkidZyXMnToUDRo0ADZ2dnw9/eHi4sL3nrrLaljFVhsbCyioqKwcOFCuLq64rPPPoO9vT169OghdbQXtmfPHnTu3FnqGIWmUqkwbtw4jB49Gl5eXihfvjwqV64sdaxCq1+/PhYuXAidTgelUomLFy/mKvinWc0eOu8qV/KcOHECJ0+exOTJk6WO8sJatmyJNWvW4M6dO9i4caPUcQrtwIEDUCqVCAsLQ2RkJKKjo7Fu3TqpY72Qx8eabW1t8eabb+LkyZMSJyocFxcXVKtWDa6uObcZbdy4MU6dOiVxqpezb98+dOrUSeoYhXbp0iWsWbMGK1euxNy5c+Hh4YFvv/1W6liFVqlSJXzxxRdYtmwZ1q9fj5o1az630K1mD/3JO88plUqcPXsW/fr1kzpWqRUeHo4zZ85gypQpePDgAWJjY01ftGMN/vnnH9y5cwetW7cGkPOLc+fOHWlDvYDhw4eb/q3T6ZCeno4BAwZIF+gFXbt2DWfPnkXPnj0BADdv3kSHDh0kTlU4DRs2hEajQXZ2NmxtbREbG4uqVatKHeuFnThxAo0aNYK9vb3UUQotLi4OarUadnY5Fefl5YV79+5JnOrFqNVqjB49GgDw2Wefwd/fP99lrerGMnndec7anDp1Cjt37sTRo0fRt29fDBo0yOrOvLx48SICAwNRr149ADnHp/z9/a1qavHWrVuYP38+6tSpA4PBgGvXrmHq1Knw8vKSOtoL+e2337Bx40ZkZWXB398fXbp0kTpSocTFxeGLL75AnTp1oNVqYTAYMGnSJNjYWM0kIoCcGZMTJ07Aw8MD9+7dw7Rp06zu9/uxMWPGYOrUqShTpozUUQotOzsbM2fOhIODA1xdXXH16lVMnjwZr7zyitTRCs3f3x9vvvkm7O3tUaNGDXTs2DHfZa2q0ImIiChv1vXnLxEREeWJhU5ERCQDLHQiIiIZYKETERHJAAudiIhIBljoRDIQHByM6dOnSx2DiCTEQieycvHx8bh37x727t2LjIwMqeMQkUR4HTqRlQsLC4Ovry/GjRuHTz75BN26dQMAbNiwAfv370fNmjWhUCiwf/9+jBgxAn379sUvv/yCiIgIqNVqxMXFYfz48XneVCe/dQghsGzZMrz//vu4c+cOTp06hVmzZqF27dpYuHAhypcvj3v37uHDDz9E8+bNsW7dOixZsgS7d++GjY0NJk+ejPLly2Pu3LnYtGkTli1bhnfffRdGoxFXr17FO++8g8DAwGLekkRWzpJf/UZElhccHCyEEGLRokUiICBACCHE5cuXRbNmzURGRoYQQoiFCxeaXrt27Zro1KmT6aslt2zZIsaNG/fMep+3DiGEmDBhghg1apQQQojIyEjx119/id69e5u+Wlej0YhmzZqJ+Ph4IYQQbdq0Ebdv3xZCCLFt2zYxYcIE07oCAgLEokWLhBA5X5XcrFkzcfny5aLYPESlBqfciazYmTNn4OPjAwDo0aMHzpw5g5s3b+LkyZOoV6+e6bajT379ZUREBHQ6HWbMmIHp06fjxIkTyMzMfGbdz1vHY4+/ZtPX1xevvfYazp07B19fXwCAu7s7ypUrhzNnzhRoLI9/zsnJCfXq1bO6L2chkprVfDkLET1r586dMBqNmDVrFoCcL6HYtm0bPD09oVAo8vwZIQSqVq2KL774wvTck99k+ORy+a3jMaVS+cxzT//Mk4/FoyN8BoPhuestyHsTUW7cQyeyUmlpacjMzMTs2bMxZcoUTJkyBePHj8eOHTvQtGlTXLhwwbTnHRkZafq5Zs2a4eLFi9BqtQCAv/76C3PmzHlm/c9bR15UKhV8fX1NyyUnJyMuLg6NGzcGkPPHxoMHDwDkfL3l06KiogAAGRkZ+PPPP9GkSZPCbA6iUo976ERWKDMzE2PHjkVaWhri4uJQrlw5AMDVq1fx4MEDhIWFISgoCEOGDMEbb7wBGxsb09dgVq9eHdOmTcP48eNRuXJlpKSkYNy4cc+8R+3atREcHJznOg4fPozz58/j/v37cHd3R7t27QAA8+fPx1dffYXTp0/j/v37mDdvHsqWLQsAGDp0KL788ks0atQIDg4OOHHiBH777Te8++67AHK+tW/WrFm4dOkSgoOD4e3tbfHtSCQnPMudSKaOHDmCVq1aAQA2btyIu3fvYvz48cW+joIIDAxESEgImjZtWuTrJiotuIdOJFNbtmzB0aNHoVAokJycjKlTp0qyDnM2btyIGzduYO3atahSpQrKly9f5O9BVBpwD52IiEgGeFIcERGRDLDQiYiIZICFTkREJAMsdCIiIhlgoRMREckAC52IiEgG/h8BhliFrwZWqQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "agegroupmean = np.array([0.1, 0.133, 0.250, 0.333, 0.462, 0.625, 0.765, 0.800])\n",
     "group = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n",
@@ -2224,12 +2599,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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am5uFvddT60OHDkVFRQWsVisAICcnB+PHj4fBYIDRaGwzduvWrZg6darrdnZ2Ng4cOOC6XVJSgqQkrpFMRDf2j0PFsNqceGhMGkKUvDwpEdAJW+RhYWFYtmwZ3nzzTWg0GqSnpyMrKwsrV65EdHQ0Zs+eDQDIz89Hampqm2n4mJgYrFu3DmfOnMGVK1cwefJkjBgxwtuSiCgAXakz48DxCsRrwjAmk6ebEbWSCIIgiF1ER3k6RcPpHPfYG/fYG/fE7M3mrwqw/3gFnn8wA6MGxYtSw83w+8Y99sY9n5haJyLqavVGC749VYm46DCMHBAndjlEPoVBTkQ+b893ZbA7nPjZqGRIpVz8hehaDHIi8mnmZjv+k1uGSJUSPx3SQ+xyiHwOg5yIfNr+4+Vosjhw94heUHi4fgRRMGGQE5HPstmd2P1dKUKVMkwYlih2OUQ+iUFORD4rO68S9UYrxg9NRHioQuxyiHwSg5yIfJIgCPj66CXIpBLcPZILRRG5wyAnIp9UeMmAy3ozRg6IgyYiROxyiHwWg5yIfNK+4+UAgPHcN050UwxyIvI59SYrvi+sRqJOhX69osQuh8inMciJyOd8c7ICDqeA8UMT21wtkYiuxyAnIp/idArYf7wCSoUUWRlcAIaoPQxyIvIpp4tqUVPfjNGD4hEe6vUFGokCHoOciHzKvlwe5EbUEQxyIvIZtQ3NOHGhBmk9I5DaI1Lscoj8AoOciHzGt6cuQxCAcUO5NU7kKQY5EfkEQRCQnVcFpVzKa44TdQCDnIh8QnFlIyprzRjaLxZhITzIjchTDHIi8gnZeZUAwFPOiDqIQU5EonM4nTh6pgrqMAUy0mLELofIrzDIiUh0eUV1aDDbMGpgPOQy/loi6gj+xBCR6A5fnVYfPThe5EqI/A+DnIhE1WSxI+dsNeI0Yejdk+eOE3UUg5yIRJV7rhpWuxNZGT14gRSiW8AgJyJRZedVAQBGZ3BanehWMMiJSDSNZivOFNcirWck4jXhYpdD5JcY5EQkmtxzNRAEcCU3Ii8wyIlINN8XVgMAhqfrRK6EyH8xyIlIFOZmO84U1yI5To246DCxyyHyW52yoPGhQ4ewe/duaLVaSCQSzJkzp83jW7duxaeffoqQkBAAwPTp0/Hwww8DAHbs2IH8/HxIpVIkJydjxowZnVESEfm4Exdq4HAKuJ1b40Re8TrIm5qasHTpUuzatQtKpRJz585FdnY2srKy2ox7++230atXrzb3VVZW4v3338f27dshkUgwffp0jB49Gqmpqd6WRUQ+7odpde4fJ/KG11Prx48fR0JCApRKJQBg+PDh2Ldv33XjtmzZgk2bNmHdunUwGAwAgIMHDyIjI8N17uiwYcNw4MABb0siIh9nsTpw+qIePbXhSIxViV0OkV/zeotcr9dDpfrhB1GtVkOv17cZM3LkSIwfPx4xMTHYv38/XnzxRWzevBm1tbVtnqtSqa57LhEFnlMX9bDanZxWJ+oEXge5VquFyWRy3TYajdBqtW3GJCUlub4ePXo0XnjhBTgcDsTExKCkpMT1mMlkQnJycrvvqdGEQy6XeVSfThfh0bhgxN64x9641xm9Of11IQBg4qjUgOp1IH2WzsbeuOdtb7wO8qFDh6KiogJWqxVKpRI5OTl4/PHHYTAYIJfLoVarsWbNGrz44ouQy+UoLi5Gr169IJPJMHbsWHz88ccQBAESiQS5ubmYOXNmu+9ZV2f2qDadLgLV1Y3efsSAxN64x9641xm9sdmdOJpXidioUEQqpQHTa37fuMfeuOdpb24W9l4HeVhYGJYtW4Y333wTGo0G6enpyMrKwsqVKxEdHY3Zs2cjNjYWy5YtQ69evXD27FmsXLkSANCjRw88++yzWL58OWQyGR555BEe6EYU4M4U16LZ6sC4oQlcW52oE0gEQRDELqKjPP3Ljn8FusfeuMfeuNcZvfnwfwtw4EQFXp45HP16RXdOYT6A3zfusTfudcYWOReEIaJuIwgCTl3UQx2mQJ+EKLHLIQoIDHIi6jalV4yoa7RgSO8YSKWcVifqDAxyIuo2Jy60nF6a2SdW5EqIAgeDnIi6zckLNZBKJBjcO0bsUogCBoOciLpFo9mKi+UN6JsYCVWoQuxyiAIGg5yIusWpi3oIADL7clqdqDMxyImoW5x07R/XtjOSiDqCQU5EXc7hdOL0xVpoI0N4kRSiTsYgJ6Iud76sHmaLHZl9YrmaG1EnY5ATUZfjtDpR12GQE1GXO3lBD4VcigEpGrFLIQo4DHIi6lK1Dc0orzFhYIoGIQrPLj9MRJ5jkBNRl8orqgUAZKRxERiirsAgJ6IulVd8NchTGeREXYFBTkRdxikIOFNcB01ECHpqw8UuhyggMciJqMtcqmqEscmGjNQYnnZG1EUY5ETUZVr3jw9K49HqRF2FQU5EXeZMcR0AYBD3jxN1GQY5EXUJi82Bc2UGJMerERmuFLscooDFICeiLnG21AC7Q+BpZ0RdjEFORF3Cdf44p9WJuhSDnIi6RF5xLRRyKfr1ihK7FKKAxiAnok5nMFpQXm1CelI0FHIuy0rUlRjkRNTpXKedcVqdqMsxyImo07WedsYD3Yi6HoOciDqVIAgouFSHiHAFEnUqscshCngMciLqVFfqmlDXaEF6sgZSLstK1OUY5ETUqfIvtUyrD0yOFrcQoiDBICeiTlVQ0hLkA1K4vjpRd2CQE1Gnadk/bkCUWokeMbxsKVF3kHfGixw6dAi7d++GVquFRCLBnDlz2jy+YcMG1NTUIDY2Fnl5eZg3bx769OkDALjrrruQmJgIAIiLi8OaNWs6oyQiEkGF3owGkxWjB8XzsqVE3cTrIG9qasLSpUuxa9cuKJVKzJ07F9nZ2cjKynKNMZvNePnllyGRSPDPf/4Tq1atwrvvvgsAmDp1KubOnettGUTkAzitTtT9vJ5aP378OBISEqBUtlzdaPjw4di3b1+bMS+99JLrr3On04nw8B+m3I4dO4aNGzdi7dq1yMnJ8bYcIhIRg5yo+3m9Ra7X66FS/XCuqFqthl6vv+FYq9WKbdu2YenSpa77FixYgMzMTDQ1NWHq1Kl47733kJKSctP31GjCIfdw2UedLsKjccGIvXGPvXHPXW+cTgFnywzQacIwqK8uKKfW+X3jHnvjnre98TrItVotTCaT67bRaIRWq71unNVqxbJly/DrX/8aycnJrvszMzMBAGFhYRg4cCBycnLaDfK6OrNHtel0EaiubvRobLBhb9xjb9y7WW8uVTWi0WxDZh8tamqM3VyZ+Ph94x57456nvblZ2Hs9tT506FBUVFTAarUCAHJycjB+/HgYDAYYjS0/zM3NzVi6dCn+67/+C4MHD8bXX38NAMjOzsaBAwdcr1VSUoKkpCRvSyIiEbim1ZM5rU7UnbzeIg8LC8OyZcvw5ptvQqPRID09HVlZWVi5ciWio6Mxe/ZsLFiwAOfOnUNZWRmAloPfJk+ejJiYGKxbtw5nzpzBlStXMHnyZIwYMcLrD0VE3a/gkgEAMJD7x4m6lUQQBEHsIjrK0ykaTue4x964x9645643DqcT8/5wEBHhSvz++awbPDPw8fvGPfbGPZ+YWiciulRlRJPFwWl1IhEwyInIa2dLDQCAdK6vTtTtGORE5LXCq/vH05OiRa2DKBgxyInIK05BwLkyA2KjQhETGSp2OURBh0FORF4przbB1GzntDqRSBjkROSVwqvXH+/PaXUiUTDIicgrPxzoxiPWicTAICeiWyYIAs6WGqCJCIEuivvHicTAICeiW1ZZa0aD2Yb0pOigvEgKkS9gkBPRLWs97aw/D3QjEg2DnIhumWv/OA90IxINg5yIbokgCCgsNSAyXIEeMeFil0MUtBjkRHRLqg1NqGu0oD/3jxOJikFORLekkKedEfkEBjkR3ZKzXF+dyCcwyInolpwtM0AVKkeCTiV2KURBjUFORB1W12hBtaEZ/XpFQ8r940SiYpATUYe1nnbWLylK3EKIiEFORB13tswAAOjfK1rUOoiIQU5Et+BcqQFKuRQpPSLELoUo6DHIiahDjE02lFeb0DshEnIZf4UQiY0/hUTUIefL6iGA1x8n8hUMciLqENf+cQY5kU9gkBNRh5wrNUAmlaBPAo9YJ/IFDHIi8liz1Y7iykYkx0cgRCkTuxwiAoOciDrg7KU6OJwC+vP8cSKfwSAnIo/lXdAD4PnjRL6EQU5EHssragnyfjzQjchnMMiJyCN2hxMFJXVIiFVBHaYQuxwiukreGS9y6NAh7N69G1qtFhKJBHPmzGnzuMViwYoVKxAfH4/i4mLMnj0baWlpAIAdO3YgPz8fUqkUycnJmDFjRmeURESd7FKVERarA/17cf84kS/xOsibmpqwdOlS7Nq1C0qlEnPnzkV2djaysrJcYzZv3oyePXviueeeQ2FhIV555RV88sknqKysxPvvv4/t27dDIpFg+vTpGD16NFJTU70ti4g62Q8XSokWtQ4iasvrqfXjx48jISEBSqUSADB8+HDs27evzZh9+/Zh2LBhAID09HQUFBTAaDTi4MGDyMjIgOTqZRCHDRuGAwcOeFsSEXWBc7xQCpFP8nqLXK/XQ6VSuW6r1Wro9XqPxtTW1ra5X6VSXffcG9FowiGXe3YOq07Hizq4w964x9605XQKOF/eAJ0mDAP66sQux2fx+8Y99sY9b3vjdZBrtVqYTCbXbaPRCK1W69GYmJgYlJSUuO43mUxITk5u9z3r6swe1abTRaC6utGjscGGvXGPvbleeY0JjWYrbh/Qi71xg9837rE37nnam5uFvddT60OHDkVFRQWsVisAICcnB+PHj4fBYIDRaAQAjB8/Hrm5uQCAwsJCDBgwAGq1GmPHjkVeXh4EQQAA5Obm4s477/S2JCLqZOeu7h8f1Ft784FE1O283iIPCwvDsmXL8Oabb0Kj0SA9PR1ZWVlYuXIloqOjMXv2bDz11FNYsWIF1q9fj0uXLuGtt94CAPTo0QPPPvssli9fDplMhkceeYQHuhH5oNYLpWSkxYhbCBFdRyK0bg77EU+naDid4x574x57c73frv8WFpsTn/x/96Kmxih2OT6J3zfusTfu+cTUOhEFNn19M/QNFvTrFeU6w4SIfAeDnIhuqnVavR9POyPySQxyIrqp1gPd+nMhGCKfxCAnops6W1YPpUKK5Hi12KUQ0Q0wyInILWOTDRU1JvRJiIJcxl8XRL6IP5lE5JZrWVZOqxP5LAY5Ebl1rrQeANCPVzwj8lkMciJy62yZATKpBH0SGOREvopBTkQ31Gy1o6SyESk9IhCi9OwiRUTU/RjkRHRDFyoa4HAK3D9O5OMY5ER0Q2cvGQDwQDciX8cgJ6IbOltqgAQ80I3I1zHIieg6NrsTFyoa0CtODVWoQuxyiOgmGOREdJ2iyw2wO5ycVifyAwxyIrpO60Iw6QxyIp/HICei6xRevVBKPwY5kc9jkBNRGw6nE+fL6tEjJhxRKqXY5RBROxjkRNRG6RUjmq0O7h8n8hMMciJqo/X8ce4fJ/IPDHIiaqN1/zi3yIn8A4OciFycgoBzZfXQRoZCGxUqdjlE5AEGORG5XK4xwdhk49Y4kR9hkBORS+u0enpytKh1EJHnGORE5FLQeqAbg5zIbzDIiQgAIAgCCi/VQRMRgrjoMLHLISIPMciJCABQUWNCo9mGAcnRkEgkYpdDRB5ikBMRgGun1TXiFkJEHcIgJyIAQOGlOgDAAO4fJ/IrDHIiglMQUHDJAE1ECHTcP07kVxjkRISKq+ePD0jWcP84kZ+Re/Nkg8GANWvWICkpCcXFxZg/fz5iY2PbjDl58iQ2b96MQYMGoaioCJmZmXj00UcBAK+//jqKiopcY1999VWkp6d7UxIR3YLCq/vHOa1O5H+8CvK3334bWVlZmDJlCv79739jxYoVWLVqVZsx1dXVePrpp5GZmQmbzYaf/OQnmDRpEmJiYqDT6fDGG2949QGIyHsFV/ePp6fwQDcif+NVkO/fvx8vvPACAGD48OFYvHjxdWMmTpzY5rZMJoNCoQAAmEwm/PnPf4ZMJkN4eDhmzJgBudyrkoiog5yCgMJLBsREhkDH9dWJ/E67qTlr1izU1NRcd/+8efOg1+uhUqkAAGq1GvX19bDb7W7DeMuWLfjFL36BiIgIAMADDzyA9PR0yOVyrFy5Eu+99x5+9atftVu0RhMOuVzW7jgA0OkiPBoXjNgb94KpN8WXG2BssuGujCTExUW2Oz6YetNR7I177I173vam3SDftGmT28e0Wi1MJhMiIyNhNBoRFRXlNsR37twJs9mMX/7yl677MjIyXF+PHj0aGzdu9CjI6+rM7Y4BWppTXd3o0dhgw964F2y9yT5eBgBIiVO1+7mDrTcdwd64x96452lvbhb2Xh21Pm7cOOTm5gIAcnJyMG7cOACA0+lERUWFa9znn38OvV6PX/7ylygsLHQd4LZixQrXmJKSEqSkpHhTDhHdgh8OdOP+cSJ/5NUO6fnz52P16tUoLi5GaWkpFi1aBAAoLCzEwoULsXPnTvzrX//C73//ewwaNAh79+6FwWDAq6++irS0NNTV1WH16tUIDQ1FUVERXn755U75UETkmZbzx+ugjQxBLPePE/kliSAIgthFdJSnUzScznGPvXEvmHpTUtmI3314DGOG9MSz9w1sd3ww9aaj2Bv32Bv3RJ9aJyL/dqa4FgAwKJXT6kT+ikFOFMRag3xgaozIlRDRrWKQEwUpm92Bs2X16KVTIUqlFLscIrpFDHKiIHW+vAE2uxODuDVO5NcY5ERBivvHiQIDg5woSJ0proNMKkH/pGixSyEiLzDIiYKQqdmG4soG9E6IRKiS1zcg8mcMcqIgVFBigCCA+8eJAgCDnCgInSnh/nGiQMEgJwpCZ4rrEKKUIa1n+1c7IyLfxiAnCjK1Dc2oqjVjQFI05DL+CiDyd/wpJgoyea7Tzrh/nCgQMMiJgszpi1eDPI1BThQIGOREQcThdCKvqBbayBAkaMPFLoeIOgGDnCiIXKxogNlix5DeWkgkErHLIaJOwCAnCiKnLuoBAEN6a0WuhIg6C4OcKIiculALmVSCASk8f5woUDDIiYJEvdGCkqpG9E+KRlgIl2UlChQMcqIgcbqo5Wh1TqsTBRYGOVGQ+GH/OE87IwokDHKiINB62llMZAgSYlVil0NEnYhBThQEiioaYWrmaWdEgYhBThQETvK0M6KAxSAnCgKnLuohk0owkKedEQUcBjlRgDMYLSipbES/XlE87YwoADHIiQLc8XM1AIBh/XQiV0JEXYFBThTgcl1BHityJUTUFRjkRAGsyWJHfkktkuPUiI0OE7scIuoCDHKiAHbqoh52h4Bh/TmtThSovDryxWAwYM2aNUhKSkJxcTHmz5+P2Njrp+/uuusuJCYmAgDi4uKwZs0aAEBZWRnWr1+PlJQUlJeXY9GiRVCpuFgFUWfhtDpR4PNqi/ztt99GVlYWZs+ejUmTJmHFihU3HDd16lR89NFH+Oijj1whDgBLly7FjBkz8Pzzz6Nfv37YuHGjN+UQ0TXsDidOXqiBNjIUSXFqscshoi7iVZDv378fw4YNAwAMHz4c+/fvv+G4Y8eOYePGjVi7di1ycnIAADabDUeOHMGQIUPafT4RdVzBpTo0WRwY1j+Wq7kRBbB2p9ZnzZqFmpqa6+6fN28e9Hq9aypcrVajvr4edrsdcnnbl12wYAEyMzPR1NSEqVOn4r333kNYWBhCQ0Ndv2DUajX0er1HRWs04ZDLZR6N1ekiPBoXjNgb9wKhN/kHLgIA7hqZ0qmfJxB601XYG/fYG/e87U27Qb5p0ya3j2m1WphMJkRGRsJoNCIqKuq6EAeAzMxMAEBYWBgGDhyInJwc3H///WhuboYgCJBIJDAajdBqPVs+sq7O7NE4nS4C1dWNHo0NNuyNe4HQG6cgIPtkBVShcugiFJ32eQKhN12FvXGPvXHP097cLOy9mlofN24ccnNzAQA5OTkYN24cAMDpdKKiogIAkJ2djQMHDrieU1JSgqSkJCgUCowaNQqnTp267vlE5J2SykYYjFYM7RsLmZQnpxAFMq+OWp8/fz5Wr16N4uJilJaWYtGiRQCAwsJCLFy4EDt37kRMTAzWrVuHM2fO4MqVK5g8eTJGjBgBAPjd736HP/3pT/jmm29w+fJlLF682PtPRETIOVsNADztjCgISARBEMQuoqM8naLhdI577I17/t4bQRCw+L1sNJhsWDtvDEIUnh1P4gl/701XYm/cY2/cE31qnYh8z8XLDag2NGNY/9hODXEi8k0McqIAc+RMFQDgjoHxIldCRN2BQU4UQJxOAccKrkAVKsfgtBixyyGibsAgJwoghaUG1ButuD09DnIZf7yJggF/0okCSOu0+qiBcSJXQkTdhUFOFCDsDie+L7yCKLUS6ckascshom7CICcKEHlFtTA12zFyQBykUq6tThQsGOREAeJIfuu0Oo9WJwomDHKiAGCxOZB7rgaxUaHonRApdjlE1I0Y5EQB4PvCK7BYHRidEc9LlhIFGQY5UQA4cOIyAGBMZoLIlRBRd2OQE/m5ylozzpYaMDBFg7joMLHLIaJuxiAn8nMHT7RcMnjsbT1FroSIxMAgJ/JjdocT356uhCpUjtt5yVKioMQgJ/JjJy/o0WCyIiujBxRyXumMKBgxyIn82AHXtDoPciMKVgxyIj9V29CMUxf1SOsZiaQ4tdjlEJFIGOREfurbU5chCDzIjSjYMciJ/JDd4cR/cssRopRxSVaiIMcgJ/JDxwquwGC0YmxmT4SFyMUuh4hExCAn8jOCIGD30VJIJMCkEUlil0NEImOQE/mZs6UGlFQ1Ynh/HVdyIyIGOZG/2X2sFABwz0hujRMRg5zIr1TVmXH8XA3Sekaib2KU2OUQkQ9gkBP5kX8dK4MAYPIdSbxcKREBYJAT+Q1Tsw0HT1VAGxmC29O5rjoRtWCQE/mJr4+WwmpzYtKIJMik/NElohb8bUDkBxrNVuz5rhSRKiXGD0sUuxwi8iEMciI/8NWRS7BYHbgvKwUhCl7ljIh+4NWSUAaDAWvWrEFSUhKKi4sxf/58xMbGthlz5MgRvPHGG4iJiQEA6PV63HvvvZg7dy5ef/11FBUVuca++uqrSE9P96YkooBTb7Rg7/dl0ESEYPxQXuWMiNryKsjffvttZGVlYcqUKfj3v/+NFStWYNWqVW3GxMXFYdWqVRg0aBAAYMmSJZg2bRoAQKfT4Y033vCmBKKAt+twCax2J37+k1Rec5yIruNVkO/fvx8vvPACAGD48OFYvHjxdWPS0tJcX9fU1MBqtSIxsWUfn8lkwp///GfIZDKEh4djxowZkMu5bjRRq9qGZuzLrUBsVCjGZvIqZ0R0vXZTc9asWaipqbnu/nnz5kGv10OlUgEA1Go16uvrYbfb3YbxJ598ghkzZrhuP/DAA0hPT4dcLsfKlSvx3nvv4Ve/+lW7RWs04ZB7uGWi00V4NC4YsTfu+UpvPt9/EXaHE49PHoCePXxjARhf6Y0vYm/cY2/c87Y37Qb5pk2b3D6m1WphMpkQGRkJo9GIqKgotyFutVpx+vRpzJs3z3VfRkaG6+vRo0dj48aNHgV5XZ253TFAS3Oqqxs9Ghts2Bv3fKU3pVeM+OpwMeJjwjEkNdonavKV3vgi9sY99sY9T3tzs7D36qj1cePGITc3FwCQk5ODcePGAQCcTicqKirajN25cyfuu+++NvetWLHC9XVJSQlSUlK8KYcoYAiCgI93F0IQgCcm9eN540Tkllc7pOfPn4/Vq1ejuLgYpaWlWLRoEQCgsLAQCxcuxM6dO11jv/rqK6xfv77N8+vq6rB69WqEhoaiqKgIL7/8sjflEAWMw2eqcK6sHsP76zC4t1bscojIh0kEQRDELqKjPJ2i4XSOe+yNe2L3pslix5INh2G22PHW/x2FWB+6VKnYvfFl7I177I17ok+tE1Hn2/ltMepNVtw3OsWnQpyIfBODnMiHlFcbsee7UuiiQ3Hv6GSxyyEiP8AgJ/IRdocTG3eegcMp4PFJ/bn4CxF5hEFO5CO2HyzCpStGjM3sidv6xrb/BCIiMMiJfMLZUgP+93AJdNGhmDGxn9jlEJEfYZATiazJYsdfvjwDSIDn7s9AWAiXKSYizzHIiUQkCAK27DmLmvpm3JeVgr69fGMZViLyHwxyIhHt/b4Mh05XIrVHBB78aVr7TyAi+hEGOZFI8opr8ene84hUKTFn2hDIZfxxJKKO428OIhFU1Zrx522nIZUCc6YNQUxkqNglEZGfYpATdTNzsx1//OIkzBY7npo8AH0TuV+ciG4dg5yoGzVb7Vj7+Qlc1ptxz8gkjMnsKXZJROTnGORE3cRidWDt5ydxvrwedwyMw6MT+opdEhEFAAY5UTew2hz44xcncbbUgNvTdXjugUGQSiVil0VEAYArTxB1sWarHX/adhr5JXUY1i8Wzz+YAZmUf0MTUedgkBN1obpGC/7w+QlcumJEZh8tfvHQYJ5mRkSdikFO1EUuVTXiD38/ibpGC8YNTcATd/dniBNRp2OQE3WB7wquYNOufFhtDjw6oS8m35EEiYT7xImo8zHIiTqRxerAX/eexYETl6GUS/HCw4MxYkCc2GURUQBjkBN1kpLKRrz7jzxU1ZqRHKfG7AczkBCrErssIgpwDHIiLzVZ7NjxTRH2fl8Gh1PAPSOTMH1cHyjk3B9ORF2PQU50iwRBwOG8KvztP+dRb7JCFx2KJyenY3CaVuzSiCiIMMiJOkgQBJw4r8c/vi1CcWUjFHIpHh6bhntHJUMhl4ldHhEFGQY5kYecTgHHz9dg57fFKKlqBACMGBCHR8f3QWx0mMjVEVGwYpATtaPeZMU3JyuwL7cC+oZmSADcMTAO9/8kFb10arHLI6IgxyAnugGL1YETF2pwLP8Kjp+vgcMpQKmQ4s7bEnD3yCQk8mh0IvIRDHKiq+qNFpwuqkVBWQGOnamE1eYEACTGqjB+WCKyMnogPJQ/MkTkW/hbiYJWg8mKc2X1OF9uQH5xHS5dMboei9eEYeTAeNwxIA6JOhVXZSMin8Ugp4AnCALqTVaUXTGipKoRpVeMKK5sxJW6JtcYuUyCjFQNMtK0uPP2JITJwPAmIr/AIKeAYHc4UW+0oqa+CTX1zag2NKHa0IzKWhMqa81osjjajA8PkWNIby369opCv8QopCVEIkTRcuqYTheB6upGMT4GEVGHeRXkTqcTf/vb3/CHP/wBmzdvRv/+/W84bseOHcjPz4dUKkVycjJmzJgBACgrK8P69euRkpKC8vJyLFq0CCoVDyIKdk6ngCarHU0WO5osDpibbTA122Fqavm30WxFg9mKBpMN9SYLDI0WNJptEG7wWnKZBPGacPRIDUdirArJ8RFIjlNDGxXKLW4iCgheBXlBQQFuu+02hIW5P4e2srIS77//PrZv3w6JRILp06dj9OjRSE1NxdKlS/Hiiy8iMzMTH330ETZu3IiXXnqp3fdtNFs9qk9ptHg89lo3CoRbGXzdQ4Lg9jHhZq9z7fOE1ue7voDQ+npXHxSuvb/1PqHl69bHjDYnamtNEATAeXWMUxBc45xOAc7WrwUBTue1X7c85nA64XQKcFz9z+kU4HAIsDudsDsEOBwt/9odTtd/NrsTVvvVf20OWK/+a7E50Gx1wGJtuc9TSrkU0REh6KlVQRMRgpjIUMRGh0IXFYbY6FDERoVCJuVSqUQUuLwK8kGDBrU75uDBg8jIyHBt/QwbNgwHDhxAYmIijhw5giFDhgAAhg8fjldffdWjIH/xj994Uzb5ELlMihCFFEqFDKpQBbSRoQhRyBAWIr/6nwzhoXKoQxVQhSmgClUgIlyBCJUSkeEKhChk3LImoqDWbpDPmjULNTU1190/b948TJw4sd03qK2tbTNdrlKpoNfrUVdXh9DQH6Y31Wo19Hq9R0XvXPOQR+OIbpVOFyF2CT6LvXGPvXGPvXHP2960G+SbNm3y6g1iYmJQUlLium0ymZCcnAyNRoPm5mYIggCJRAKj0QitlhebICIi6ogu2XnodDpRUVEBABg7dizy8vJc+2pzc3Nx5513QqFQYNSoUTh16hQAICcnB+PGjeuKcoiIiAKWRBBudpjVzdXX12PLli344IMP8NBDD+H+++/H0KFDkZ+fj4ULF2Lnzp0AWo5aP336NGQyGVJTU9sctf6nP/0JSUlJuHz5MhYvXsyj1omIiDrAqyAnIiIicfG8HCIiIj/GICciIvJjAb9Eq9VqxQcffICwsDCcP38eGo0Gv/71r8Uuy6esX78emzdvxpEjR8QuxWcsX74cYWFhCA8PR0FBAZYsWQKdTid2WaI5dOgQdu/eDa1WC4lEgjlz5ohdks+4dOkS1q5di0GDBqGyshLR0dHszzWam5vxyCOPYMyYMVi0aJHY5fiUixcvYteuXQgJCcGxY8cwd+5cZGZmdvh1Aj7IN27ciDvuuAMjR44E0LIaHf3gyJEjaGhoELsMnxMWFub6g2/Dhg1499138dprr4lclTiampqwdOlS7Nq1C0qlEnPnzkV2djaysrLELs0nGAwGTJkyBZMmTQIATJkyBePHj8fgwYNFrsw3tP6RQ205HA78/ve/x7vvvgupVIqHH34YcvmtRXLAB/mXX36JhIQE5OXlwWAw4MknnxS7JJ9RU1ODXbt2Yfbs2di2bZvY5fiUa2dtBEFAeHi4iNWI6/jx40hISIBSqQTQsgrjvn37GORX/XgLyul03nTZ6mCyfft2DB8+HIWFhTCbzWKX41NOnToFQRDw0Ucfobm5GdHR0Xj00Udv6bUCIshvtvpceXk5JBIJnnnmGRw6dAgvvfQSPvroIxGqFMfNerN3714sWrQIjY3BeaUvT1YtbGhowDfffIN33nmnu8vzGXq9vs1poR1ZhTHY7NmzB2PGjEGfPn3ELkV058+fx8WLFzF//nwUFhaKXY7PqaiowPHjx/H2228jIiICCxYsgEKhwLRp0zr8WgER5DdbfU6tVrv+Yr799tvx3XffweFwQCaTdVd5onLXm1OnTkEul+Ozzz5DfX09LBYLNmzYgHvuuQepqandW6RI2lu1sLGxEb/73e+wfPlyREdHd09RPkir1cJkMrlucxXGGzt8+DCOHDmCJUuWiF2KT9izZw+USiU2bNiA77//HjabDR9++CGeeeYZsUvzCSqVCr1790ZERMvyrLfffjuOHj0avEF+M1lZWSgtLUXv3r1RXl6O5OTkoAnxmxkyZIjrgjVlZWX4+9//jtmzZ4tcle+ora3F8uXLsXDhQsTHx+Prr7/G5MmTxS5LFEOHDkVFRQWsViuUSiVycnLw+OOPi12WT9m3bx++++47vPLKK7hy5QoqKiowbNgwscsS1QsvvOD62mKxwGw2M8Svcdttt8FgMLg2LCsqKm55IyrgF4SpqqrCH//4RyQnJ+PChQuYOXPmLR0VGKhKSkrw6aef4q9//Stmz56NZ555Jqj3B7eaOnUq7Ha7a0tcpVLh3XffFbcoEX377bf4+uuvodFooFAoeFT2NU6fPo0nn3zSdXCb2WzGE088cUtbVoHo66+/xpYtW2Cz2fDEE0/g/vvvF7skn7Fnzx4cPnwYGo0Gly9fxmuvvYbQ0NAOv07ABzkREVEg44IwREREfoxBTkRE5McY5ERERH6MQU5EROTHGORERER+jEFORETkxxjkREREfizgV3YjIu9t3boVe/fuRXx8POrr6/HPf/4TO3bsQP/+/cUujSjocUEYImpXbm4uoqOjkZaWhrlz5yI5ORm//e1vxS6LiMAgJ6IO+Pzzz/HJJ5/gs88+c13WlIjExal1IvJIUVER1qxZgy1btjDEiXwID3YjonbZbDb85je/wdy5c9GnTx9cuHAB3333ndhlERG4RU5EHvjwww9x4cIFXLhwAW+88QaqqqowceJEjBgxQuzSiIIe95ETERH5MU6tExER+TEGORERkR9jkBMREfkxBjkREZEfY5ATERH5MQY5ERGRH2OQExER+TEGORERkR/7f9MT3NIEGTV6AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "\"\"\"The sigmoid function (or the logistic curve) is a\n",
     "function that takes any real number, z, and outputs a number (0,1).\n",
@@ -2647,10 +3050,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -2692,12 +3092,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x1440 with 30 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -2759,10 +3177,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "cancerpd = pd.DataFrame(cancer.data, columns=cancer.feature_names)"
@@ -2778,10 +3193,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "correlation_matrix = cancerpd.corr().round(1)"
@@ -2803,12 +3215,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(426, 30)\n",
+      "(143, 30)\n",
+      "Test set accuracy with Logistic Regression: 0.94\n",
+      "Test set accuracy Logistic Regression with scaled data: 0.96\n",
+      "[1.         1.         1.         1.         1.         1.\n",
+      " 1.         1.         0.92857143 0.92857143]\n",
+      "Test set accuracy with Logistic Regression  and scaled data: 0.96\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/Software/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'scikitplot'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-12adb44b1c20>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mscikitplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mskplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_scaled\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0mskplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_confusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'scikitplot'"
+     ]
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -3292,7 +3740,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 4
 }
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index e82807fa8..090e01eaf 100644
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@@ -1,31 +1,28 @@
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+              ('Schematic Regression Procedure',
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               ('Cancer Data again now with Decision Trees and other Methods',
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-              ('Using the Voting Classifier', 2, None, '___sec42'),
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@@ -145,58 +201,57 @@ MathJax.Hub.Config({
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     <a class="navbar-brand" href="week44-bs.html">Week 44: From Decision Trees to Bagging methods</a>
   </div>
-
   <div class="navbar-collapse collapse navbar-responsive-collapse">
     <ul class="nav navbar-nav navbar-right">
       <li class="dropdown">
         <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
         <ul class="dropdown-menu">
-     <!-- navigation toc: --> <li><a href="._week44-bs001.html#___sec0" style="font-size: 80%;">Overview of week 44</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs002.html#___sec1" style="font-size: 80%;">Thursday</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs003.html#___sec2" style="font-size: 80%;">Decision trees, overarching aims</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs004.html#___sec3" style="font-size: 80%;">Basics of a tree</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs005.html#___sec4" style="font-size: 80%;">A Sketch of a Tree, Regression problem</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs006.html#___sec5" style="font-size: 80%;">A Sketch of a Tree, Classification  problem</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs007.html#___sec6" style="font-size: 80%;">A typical Decision Tree with its pertinent Jargon, Classification Problem</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs008.html#___sec7" style="font-size: 80%;">General Features</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs009.html#___sec8" style="font-size: 80%;">How do we set it up?</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs010.html#___sec9" style="font-size: 80%;">Decision trees and Regression</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs011.html#___sec10" style="font-size: 80%;">Building a tree, regression</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs012.html#___sec11" style="font-size: 80%;">A top-down approach, recursive binary splitting</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs013.html#___sec12" style="font-size: 80%;">Making a tree</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs014.html#___sec13" style="font-size: 80%;">Pruning the tree</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs015.html#___sec14" style="font-size: 80%;">Cost complexity pruning</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs016.html#___sec15" style="font-size: 80%;">Schematic Regression Procedure</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs017.html#___sec16" style="font-size: 80%;">A Classification Tree</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs018.html#___sec17" style="font-size: 80%;">Growing a classification tree</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs019.html#___sec18" style="font-size: 80%;">Classification tree, how to split nodes</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs020.html#___sec19" style="font-size: 80%;">Visualizing the Tree, Classification</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs021.html#___sec20" style="font-size: 80%;">Visualizing the Tree, The Moons</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs022.html#___sec21" style="font-size: 80%;">Other ways of visualizing the trees</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs023.html#___sec22" style="font-size: 80%;">Printing out as text</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs024.html#___sec23" style="font-size: 80%;">Algorithms for Setting up Decision Trees</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs025.html#___sec24" style="font-size: 80%;">The CART algorithm for Classification</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs026.html#___sec25" style="font-size: 80%;">The CART algorithm for Regression</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs027.html#___sec26" style="font-size: 80%;">Computing the Gini index</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs028.html#___sec27" style="font-size: 80%;">Simple Python Code to read in Data and perform Classification</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs029.html#___sec28" style="font-size: 80%;">Computing the Gini Factor</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs030.html#___sec29" style="font-size: 80%;">Entropy and the ID3 algorithm</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs031.html#___sec30" style="font-size: 80%;">Cancer Data again now with Decision Trees and other Methods</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs032.html#___sec31" style="font-size: 80%;">Another example, the moons again</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs033.html#___sec32" style="font-size: 80%;">Playing around with regions</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs034.html#___sec33" style="font-size: 80%;">Regression trees</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs035.html#___sec34" style="font-size: 80%;">Final regressor code</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs036.html#___sec35" style="font-size: 80%;">Pros and cons of trees, pros</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs037.html#___sec36" style="font-size: 80%;">Disadvantages</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs038.html#___sec37" style="font-size: 80%;">Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs039.html#___sec38" style="font-size: 80%;">An Overview of Ensemble Methods</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs040.html#___sec39" style="font-size: 80%;">Bagging</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs041.html#___sec40" style="font-size: 80%;">More bagging</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs042.html#___sec41" style="font-size: 80%;">Simple Voting Example, head or tail</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs043.html#___sec42" style="font-size: 80%;">Using the Voting Classifier</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs044.html#___sec43" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs045.html#___sec44" style="font-size: 80%;">Bagging Examples</a></li>
-     <!-- navigation toc: --> <li><a href="._week44-bs046.html#___sec45" style="font-size: 80%;">Making your own Bootstrap: Changing the Level of the Decision Tree</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs001.html#overview-of-week-44" style="font-size: 80%;">Overview of week 44</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs002.html#thursday" style="font-size: 80%;">Thursday</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs003.html#decision-trees-overarching-aims" style="font-size: 80%;">Decision trees, overarching aims</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs004.html#basics-of-a-tree" style="font-size: 80%;">Basics of a tree</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs005.html#a-sketch-of-a-tree-regression-problem" style="font-size: 80%;">A Sketch of a Tree, Regression problem</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs006.html#a-sketch-of-a-tree-classification-problem" style="font-size: 80%;">A Sketch of a Tree, Classification  problem</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs007.html#a-typical-decision-tree-with-its-pertinent-jargon-classification-problem" style="font-size: 80%;">A typical Decision Tree with its pertinent Jargon, Classification Problem</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs008.html#general-features" style="font-size: 80%;">General Features</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs009.html#how-do-we-set-it-up" style="font-size: 80%;">How do we set it up?</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs010.html#decision-trees-and-regression" style="font-size: 80%;">Decision trees and Regression</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs011.html#building-a-tree-regression" style="font-size: 80%;">Building a tree, regression</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs012.html#a-top-down-approach-recursive-binary-splitting" style="font-size: 80%;">A top-down approach, recursive binary splitting</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs013.html#making-a-tree" style="font-size: 80%;">Making a tree</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs014.html#pruning-the-tree" style="font-size: 80%;">Pruning the tree</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs015.html#cost-complexity-pruning" style="font-size: 80%;">Cost complexity pruning</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs016.html#schematic-regression-procedure" style="font-size: 80%;">Schematic Regression Procedure</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs017.html#a-classification-tree" style="font-size: 80%;">A Classification Tree</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs018.html#growing-a-classification-tree" style="font-size: 80%;">Growing a classification tree</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs019.html#classification-tree-how-to-split-nodes" style="font-size: 80%;">Classification tree, how to split nodes</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs020.html#visualizing-the-tree-classification" style="font-size: 80%;">Visualizing the Tree, Classification</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs021.html#visualizing-the-tree-the-moons" style="font-size: 80%;">Visualizing the Tree, The Moons</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs022.html#other-ways-of-visualizing-the-trees" style="font-size: 80%;">Other ways of visualizing the trees</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs023.html#printing-out-as-text" style="font-size: 80%;">Printing out as text</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs024.html#algorithms-for-setting-up-decision-trees" style="font-size: 80%;">Algorithms for Setting up Decision Trees</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs025.html#the-cart-algorithm-for-classification" style="font-size: 80%;">The CART algorithm for Classification</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs026.html#the-cart-algorithm-for-regression" style="font-size: 80%;">The CART algorithm for Regression</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs027.html#computing-the-gini-index" style="font-size: 80%;">Computing the Gini index</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs028.html#simple-python-code-to-read-in-data-and-perform-classification" style="font-size: 80%;">Simple Python Code to read in Data and perform Classification</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs029.html#computing-the-gini-factor" style="font-size: 80%;">Computing the Gini Factor</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs030.html#entropy-and-the-id3-algorithm" style="font-size: 80%;">Entropy and the ID3 algorithm</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs031.html#cancer-data-again-now-with-decision-trees-and-other-methods" style="font-size: 80%;">Cancer Data again now with Decision Trees and other Methods</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs032.html#another-example-the-moons-again" style="font-size: 80%;">Another example, the moons again</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs033.html#playing-around-with-regions" style="font-size: 80%;">Playing around with regions</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs034.html#regression-trees" style="font-size: 80%;">Regression trees</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs035.html#final-regressor-code" style="font-size: 80%;">Final regressor code</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs036.html#pros-and-cons-of-trees-pros" style="font-size: 80%;">Pros and cons of trees, pros</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs037.html#disadvantages" style="font-size: 80%;">Disadvantages</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs038.html#ensemble-methods-from-a-single-tree-to-many-trees-and-extreme-boosting-meet-the-jungle-of-methods" style="font-size: 80%;">Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs039.html#an-overview-of-ensemble-methods" style="font-size: 80%;">An Overview of Ensemble Methods</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs040.html#bagging" style="font-size: 80%;">Bagging</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs041.html#more-bagging" style="font-size: 80%;">More bagging</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs042.html#simple-voting-example-head-or-tail" style="font-size: 80%;">Simple Voting Example, head or tail</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs043.html#using-the-voting-classifier" style="font-size: 80%;">Using the Voting Classifier</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs044.html#please-not-the-moons-again-voting-and-bagging" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs045.html#bagging-examples" style="font-size: 80%;">Bagging Examples</a></li>
+     <!-- navigation toc: --> <li><a href="._week44-bs046.html#making-your-own-bootstrap-changing-the-level-of-the-decision-tree" style="font-size: 80%;">Making your own Bootstrap: Changing the Level of the Decision Tree</a></li>
 
         </ul>
       </li>
@@ -204,36 +259,32 @@ MathJax.Hub.Config({
   </div>
 </div>
 </div> <!-- end of navigation bar -->
-
 <div class="container">
-
 <p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p> <!-- add vertical space -->
-
 <a name="part0000"></a>
 <!-- ------------------- main content ---------------------- -->
-
-
-
 <div class="jumbotron">
-<center><h1>Week 44: From Decision Trees to Bagging methods</h1></center>  <!-- document title -->
+<center>
+<h1>Week 44: From Decision Trees to Bagging methods</h1>
+</center>  <!-- document title -->
 
-<p>
 <!-- author(s): Morten Hjorth-Jensen -->
-
 <center>
 <b>Morten Hjorth-Jensen</b> [1, 2]
 </center>
-
-<p>
 <!-- institution(s) -->
+<center>
+[1] <b>Department of Physics, University of Oslo</b>
+</center>
+<center>
+[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b>
+</center>
+<br>
+<center>
+<h4>Nov 1, 2021</h4>
+</center> <!-- date -->
+<br>
 
-<center>[1] <b>Department of Physics, University of Oslo</b></center>
-<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
-<br>
-<p>
-<center><h4>Nov 5, 2020</h4></center> <!-- date -->
-<br>
-<p>
 
 
 <p><a href="._week44-bs001.html" class="btn btn-primary btn-lg">Read &raquo;</a></p>
@@ -259,25 +310,18 @@ MathJax.Hub.Config({
   <li><a href="._week44-bs001.html">&raquo;</a></li>
 </ul>
 <!-- ------------------- end of main content --------------- -->
-
 </div>  <!-- end container -->
 <!-- include javascript, jQuery *first* -->
 <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
 <script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
-
 <!-- Bootstrap footer
 <footer>
-<a href="http://..."><img width="250" align=right src="http://..."></a>
+<a href="https://..."><img width="250" align=right src="https://..."></a>
 </footer>
 -->
-
-
 <center style="font-size:80%">
-<!-- copyright --> &copy; 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
+<!-- copyright --> &copy; 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
 </center>
-
-
 </body>
 </html>
-    
 
diff --git a/doc/pub/week44/html/week44-reveal.html b/doc/pub/week44/html/week44-reveal.html
index 9bad20b90..4b62811e7 100644
--- a/doc/pub/week44/html/week44-reveal.html
+++ b/doc/pub/week44/html/week44-reveal.html
@@ -1,19 +1,18 @@
+<!--
+HTML file automatically generated from DocOnce source
+(https://github.com/doconce/doconce/)
+doconce format html week44-reveal.html week44-reveal reveal --html_slide_theme=beige
+-->
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-
+<html>
+<head>
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-<meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
+<meta name="generator" content="DocOnce: https://github.com/doconce/doconce/" />
 <meta name="viewport" content="width=device-width, initial-scale=1.0" />
 <meta name="description" content="Week 44: From Decision Trees to Bagging methods">
-
 <title>Week 44: From Decision Trees to Bagging methods</title>
 
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+<!-- reveal.js: https://lab.hakim.se/reveal-js/ -->
 
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-
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 <style type="text/css">
 td.padding {
@@ -97,12 +141,9 @@ td.padding {
 
 </head>
 
+
 <body>
 <div class="reveal">
-
-<!-- Any section element inside the <div class="slides"> container
-     is displayed as a slide -->
-
 <div class="slides">
 
 
@@ -123,141 +164,126 @@ MathJax.Hub.Config({
 
 
 
-    
-
-
 
 <section>
 <!-- ------------------- main content ---------------------- -->
+<center>
+<h1 style="text-align: center;">Week 44: From Decision Trees to Bagging methods</h1>
+</center>  <!-- document title -->
 
-
-
-<center><h1 style="text-align: center;">Week 44: From Decision Trees to Bagging methods</h1></center>  <!-- document title -->
-
-<p>
 <!-- author(s): Morten Hjorth-Jensen -->
-
 <center>
 <b>Morten Hjorth-Jensen</b> [1, 2]
 </center>
-
-<p>&nbsp;<br>
 <!-- institution(s) -->
+<center>
+[1] <b>Department of Physics, University of Oslo</b>
+</center>
+<center>
+[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b>
+</center>
+<br>
+<center>
+<h4>Nov 1, 2021</h4>
+</center> <!-- date -->
+<br>
 
-<center>[1] <b>Department of Physics, University of Oslo</b></center>
-<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
-<br>
-<p>&nbsp;<br>
-<center><h4>Nov 5, 2020</h4></center> <!-- date -->
-<br>
-<p>
 
 <center style="font-size:80%">
-<!-- copyright --> &copy; 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
+<!-- copyright --> &copy; 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
 </center>
 </section>
 
-
 <section>
-<h2 id="___sec0">Overview of week 44 </h2>
+<h2 id="overview-of-week-44">Overview of week 44 </h2>
 
 <ul>
-<p><li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober29.mp4?vrtx=view-as-webpage" target="_blank">Thursday: Wrapping up PCA from last week and basics of decision trees, classification and regression algorithms with video of lecture</a></li> 
-<p><li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober30.mp4?vrtx=view-as-webpage" target="_blank">Friday: Decision trees, voting models and bagging with video of lecture</a></li> 
+<p><li> Thursday: Wrapping up PCA from last week and basics of decision trees, classification and regression algorithms</li>
+<p><li> Friday: Decision trees, voting models and bagging</li>
 </ul>
 <p>
-
-Geron's chapter 6 covers decision trees while ensemble models, voting and bagging are discussed in chapter 7. See also lecture from <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf" target="_blank">STK-IN4300, lecture 7</a>. Chapter 9.2 of Hastie et al contains also a good discussion.
+<p>Geron's chapter 6 covers decision trees while ensemble models, voting and bagging are discussed in chapter 7. See also lecture from <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf" target="_blank">STK-IN4300, lecture 7</a>. Chapter 9.2 of Hastie et al contains also a good discussion.</p>
 </section>
 
-
 <section>
-<h2 id="___sec1">Thursday </h2>
+<h2 id="thursday">Thursday </h2>
 
-<p>
-Decision trees and wrapping up PCA.
+<p>Decision trees and wrapping up PCA.</p>
 </section>
 
-
 <section>
-<h2 id="___sec2">Decision trees, overarching aims  </h2>
+<h2 id="decision-trees-overarching-aims">Decision trees, overarching aims  </h2>
 
-<p>
-We start here with the most basic algorithm, the so-called decision
+<p>We start here with the most basic algorithm, the so-called decision
 tree. With this basic algorithm we can in turn build more complex
 networks, spanning from homogeneous and heterogenous forests (bagging,
 random forests and more) to one of the most popular supervised
 algorithms nowadays, the extreme gradient boosting, or just
 XGBoost. But let us start with the simplest possible ingredient.
+</p>
 
-<p>
-Decision trees are supervised learning algorithms used for both,
+<p>Decision trees are supervised learning algorithms used for both,
 classification and regression tasks.
+</p>
 
-<p>
-The main idea of decision trees
+<p>The main idea of decision trees
 is to find those descriptive features which contain the most
 <b>information</b> regarding the target feature and then split the dataset
 along the values of these features such that the target feature values
 for the resulting underlying datasets are as pure as possible.
+</p>
 
-<p>
-The descriptive features which reproduce best the target/output features are normally  said
+<p>The descriptive features which reproduce best the target/output features are normally  said
 to be the most informative ones. The process of finding the <b>most
 informative</b> feature is done until we accomplish a stopping criteria
-where we then finally end up in so called <b>leaf nodes</b>.
+where we then finally end up in so called <b>leaf nodes</b>. 
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec3">Basics of a tree </h2>
+<h2 id="basics-of-a-tree">Basics of a tree </h2>
 
-<p>
-A decision tree is typically divided into a <b>root node</b>, the <b>interior nodes</b>,
+<p>A decision tree is typically divided into a <b>root node</b>, the <b>interior nodes</b>,
 and the final <b>leaf nodes</b> or just <b>leaves</b>. These entities are then connected by so-called <b>branches</b>.
+</p>
 
-<p>
-The leaf nodes
+<p>The leaf nodes
 contain the predictions we will make for new query instances presented
 to our trained model. This is possible since the model has 
 learned the underlying structure of the training data and hence can,
 given some assumptions, make predictions about the target feature value
 (class) of unseen query instances.
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec4">A Sketch of a Tree, Regression problem  </h2>
+<h2 id="a-sketch-of-a-tree-regression-problem">A Sketch of a Tree, Regression problem  </h2>
 
-<p>
 <!-- FIGURE: [DataFiles/Regsimpletree.png, width=600 frac=0.8] -->
 </section>
 
-
 <section>
-<h2 id="___sec5">A Sketch of a Tree, Classification  problem  </h2>
+<h2 id="a-sketch-of-a-tree-classification-problem">A Sketch of a Tree, Classification  problem  </h2>
 
-<p>
 <!-- FIGURE: [DataFiles/Classimpletree.png, width=600 frac=0.8] -->
 </section>
 
-
 <section>
-<h2 id="___sec6">A typical Decision Tree with its pertinent Jargon, Classification Problem </h2>
+<h2 id="a-typical-decision-tree-with-its-pertinent-jargon-classification-problem">A typical Decision Tree with its pertinent Jargon, Classification Problem </h2>
 
-<p>
-<br /><br /><center><p><img src="DataFiles/cancer.png" align="bottom" width=600></p></center><br /><br />
+<br/><br/>
+<center>
+<p><img src="DataFiles/cancer.png" width="600" align="bottom"></p>
+</center>
+<br/><br/>
 
-<p>
-This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using <b>Scikit-Learn</b>'s decision tree classifier. Here we have used the so-called <b>gini</b> index (see below) to split the various branches.
+<p>This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using <b>Scikit-Learn</b>'s decision tree classifier. Here we have used the so-called <b>gini</b> index (see below) to split the various branches.</p>
 </section>
 
-
 <section>
-<h2 id="___sec7">General Features </h2>
+<h2 id="general-features">General Features </h2>
 
-<p>
-The overarching approach to decision trees is a top-down approach.
+<p>The overarching approach to decision trees is a top-down approach.</p>
 
 <ul>
 <p><li> A leaf provides the classification of a given instance.</li>
@@ -266,18 +292,17 @@ The overarching approach to decision trees is a top-down approach.
 <p><li> An instance is classified by starting at the root node of the tree, testing the attribute specified by this node, then moving down the tree branch corresponding to the value of the attribute in the given example.</li>
 </ul>
 <p>
-
-This process is then repeated for the subtree rooted at the new
+<p>This process is then repeated for the subtree rooted at the new
 node.
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec8">How do we set it up? </h2>
+<h2 id="how-do-we-set-it-up">How do we set it up? </h2>
 
-<p>
-In simplified terms, the process of training a decision tree and
+<p>In simplified terms, the process of training a decision tree and
 predicting the target features of query instances is as follows:
+</p>
 
 <ol>
 <p><li> Present a dataset containing of a number of training instances characterized by a number of descriptive features and a target feature</li>
@@ -286,17 +311,19 @@ predicting the target features of query instances is as follows:
 <p><li> Show query instances to the tree and run down the tree until we arrive at leaf nodes</li>
 </ol>
 <p>
-
-Then we are essentially done!
+<p>Then we are essentially done!</p>
 </section>
 
-
 <section>
-<h2 id="___sec9">Decision trees and Regression  </h2>
-<p>
+<h2 id="decision-trees-and-regression">Decision trees and Regression  </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> PolynomialFeatures
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.linear_model</span> <span style="color: #8B008B; font-weight: bold">import</span> LinearRegression
@@ -334,7 +361,7 @@ c=lin_reg.intercept_
 <span style="color: #658b00">print</span> (<span style="color: #CD5555">&quot;first power: &quot;</span>, b[<span style="color: #B452CD">0</span>])
 <span style="color: #658b00">print</span> (<span style="color: #CD5555">&quot;second power: &quot;</span>,b[<span style="color: #B452CD">1</span>])
 
-z = np.arange(<span style="color: #B452CD">0</span>, steps, .<span style="color: #B452CD">01</span>)
+z = np.arange(<span style="color: #B452CD">0</span>, steps, <span style="color: #B452CD">.01</span>)
 z_mod=b[<span style="color: #B452CD">1</span>]*z**<span style="color: #B452CD">2</span>+b[<span style="color: #B452CD">0</span>]*z+c
 
 fit_mod=b[<span style="color: #B452CD">1</span>]*X**<span style="color: #B452CD">2</span>+b[<span style="color: #B452CD">0</span>]*X+c
@@ -384,29 +411,39 @@ plt.ylabel(<span style="color: #CD5555">&quot;Darget&quot;</span>)
 plt.title(<span style="color: #CD5555">&quot;Decision Tree Regression&quot;</span>)
 plt.legend()
 plt.show()
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec10">Building a tree, regression </h2>
-
-<p>
-There are mainly two steps
+<h2 id="building-a-tree-regression">Building a tree, regression </h2>
 
+<p>There are mainly two steps</p>
 <ol>
 <p><li> We split the predictor space (the set of possible values \( x_1,x_2,\dots, x_p \)) into \( J \) distinct and non-non-overlapping regions, \( R_1,R_2,\dots,R_J \).</li>
 
 <p><li> For every observation that falls into the region \( R_j \) , we make the same prediction, which is simply the mean of the response values for the training observations in \( R_j \).</li>
 </ol>
 <p>
-
-How do we construct the regions \( R_1,\dots,R_J \)?  In theory, the
+<p>How do we construct the regions \( R_1,\dots,R_J \)?  In theory, the
 regions could have any shape. However, we choose to divide the
 predictor space into high-dimensional rectangles, or boxes, for
 simplicity and for ease of interpretation of the resulting predictive
 model. The goal is to find boxes \( R_1,\dots,R_J \) that minimize the
 MSE, given by
+</p>
 
 <p>&nbsp;<br>
 $$
@@ -414,77 +451,74 @@ $$
 $$
 <p>&nbsp;<br>
 
-<p>
-where \( \overline{y}_{R_j} \)  is the mean response for the training observations 
-within box \( j \).
+<p>where \( \overline{y}_{R_j} \)  is the mean response for the training observations 
+within box \( j \). 
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec11">A top-down approach, recursive binary splitting </h2>
+<h2 id="a-top-down-approach-recursive-binary-splitting">A top-down approach, recursive binary splitting </h2>
 
-<p>
-Unfortunately, it is computationally infeasible to consider every
+<p>Unfortunately, it is computationally infeasible to consider every
 possible partition of the feature space into \( J \) boxes.  The common
 strategy is to take a top-down approach
+</p>
 
-<p>
-The approach is top-down because it begins at the top of the tree (all
+<p>The approach is top-down because it begins at the top of the tree (all
 observations belong to a single region) and then successively splits
 the predictor space; each split is indicated via two new branches
 further down on the tree. It is greedy because at each step of the
 tree-building process, the best split is made at that particular step,
 rather than looking ahead and picking a split that will lead to a
 better tree in some future step.
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec12">Making a tree </h2>
+<h2 id="making-a-tree">Making a tree </h2>
 
-<p>
-In order to implement the recursive binary splitting we start by selecting
+<p>In order to implement the recursive binary splitting we start by selecting
 the predictor \( x_j \) and a cutpoint \( s \) that splits the predictor space into two regions \( R_1 \) and \( R_2 \)
+</p>
 <p>&nbsp;<br>
 $$
 \left\{X\vert x_j < s\right\},
 $$
 <p>&nbsp;<br>
 
-and
+<p>and</p>
 <p>&nbsp;<br>
 $$
 \left\{X\vert x_j \geq s\right\},
 $$
 <p>&nbsp;<br>
 
-so that we obtain the lowest MSE, that is
+<p>so that we obtain the lowest MSE, that is</p>
 <p>&nbsp;<br>
 $$
 \sum_{i:x_i\in R_j}(y_i-\overline{y}_{R_1})^2+\sum_{i:x_i\in R_2}(y_i-\overline{y}_{R_2})^2,
 $$
 <p>&nbsp;<br>
 
-<p>
-which we want to minimize by considering all predictors
+<p>which we want to minimize by considering all predictors
 \( x_1,x_2,\dots,x_p \).  We consider also all possible values of \( s \) for
 each predictor. These values could be determined by randomly assigned
 numbers or by starting at the midpoint and then proceed till we find
 an optimal value.
+</p>
 
-<p>
-For any \( j \) and \( s \), we define the pair of half-planes where
+<p>For any \( j \) and \( s \), we define the pair of half-planes where
 \( \overline{y}_{R_1} \) is the mean response for the training
 observations in \( R_1(j,s) \), and \( \overline{y}_{R_2} \) is the mean
 response for the training observations in \( R_2(j,s) \).
+</p>
 
-<p>
-Finding the values of \( j \) and \( s \) that minimize the above equation can be
+<p>Finding the values of \( j \) and \( s \) that minimize the above equation can be
 done quite quickly, especially when the number of features \( p \) is not
 too large.
+</p>
 
-<p>
-Next, we repeat the process, looking
+<p>Next, we repeat the process, looking
 for the best predictor and best cutpoint in order to split the data
 further so as to minimize the MSE within each of the resulting
 regions. However, this time, instead of splitting the entire predictor
@@ -493,77 +527,74 @@ have three regions. Again, we look to split one of these three regions
 further, so as to minimize the MSE. The process continues until a
 stopping criterion is reached; for instance, we may continue until no
 region contains more than five observations.
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec13">Pruning the tree </h2>
+<h2 id="pruning-the-tree">Pruning the tree </h2>
 
-<p>
-The above procedure is rather straightforward, but leads often to
+<p>The above procedure is rather straightforward, but leads often to
 overfitting and unnecessarily large and complicated trees. The basic
 idea is to grow a large tree \( T_0 \) and then prune it back in order to
 obtain a subtree. A smaller tree with fewer splits (fewer regions) can
 lead to smaller variance and better interpretation at the cost of a
 little more bias.
+</p>
 
-<p>
-The so-called Cost complexity pruning algorithm gives us a
+<p>The so-called Cost complexity pruning algorithm gives us a
 way to do just this. Rather than considering every possible subtree,
 we consider a sequence of trees indexed by a nonnegative tuning
 parameter \( \alpha \).
+</p>
 
-<p>
-Read more at the following <a href="https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py" target="_blank">Scikit-Learn link on pruning</a>.
+<p>Read more at the following <a href="https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py" target="_blank">Scikit-Learn link on pruning</a>.</p>
 </section>
 
-
 <section>
-<h2 id="___sec14">Cost complexity pruning </h2>
+<h2 id="cost-complexity-pruning">Cost complexity pruning </h2>
 
-<p>
-For each value of \( \alpha \)  there corresponds a subtree \( T \in T_0 \) such that
+<p>For each value of \( \alpha \)  there corresponds a subtree \( T \in T_0 \) such that</p>
 <p>&nbsp;<br>
 $$
 \sum_{m=1}^{\overline{T}}\sum_{i:x_i\in R_m}(y_i-\overline{y}_{R_m})^2+\alpha\overline{T},
 $$
 <p>&nbsp;<br>
 
-is as small as possible. Here \( \overline{T} \) is 
+<p>is as small as possible. Here \( \overline{T} \) is 
 the number of terminal nodes of the tree \( T \) , \( R_m \) is the
 rectangle (i.e. the subset of predictor space)  corresponding to the \( m \)-th terminal node.
+</p>
 
-<p>
-The tuning parameter \( \alpha \) controls a trade-off between the subtree&#8217;s
+<p>The tuning parameter \( \alpha \) controls a trade-off between the subtree&#8217;s
 complexity and its fit to the training data. When \( \alpha = 0 \), then the
 subtree \( T \) will simply equal \( T_0 \), 
 because then the above equation just measures the
 training error. 
 However, as \( \alpha \) increases, there is a price to pay for
 having a tree with many terminal nodes. The above equation will
-tend to be minimized for a smaller subtree.
+tend to be minimized for a smaller subtree. 
+</p>
 
-<p>
-It turns out that as we increase \( \alpha \) from zero
+<p>It turns out that as we increase \( \alpha \) from zero
 branches get pruned from the tree in a nested and predictable fashion,
 so obtaining the whole sequence of subtrees as a function of \( \alpha \) is
 easy. We can select a value of \( \alpha \) using a validation set or using
 cross-validation. We then return to the full data set and obtain the
-subtree corresponding to \( \alpha \).
+subtree corresponding to \( \alpha \). 
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec15">Schematic Regression Procedure </h2>
+<h2 id="schematic-regression-procedure">Schematic Regression Procedure </h2>
 
-<p>
 <div class="alert alert-block alert-block alert-text-normal">
-<b>Building a Regression Tree.</b>
+<b>Building a Regression Tree</b>
+<p>
+
 <ol>
 <p><li> Use recursive binary splitting to grow a large tree on the training data, stopping only when each terminal node has fewer than some minimum number of observations.</li>
 <p><li> Apply cost complexity pruning to the large tree in order to obtain a sequence of best subtrees, as a function of \( \alpha \).</li>
 <p><li> Use for example \( K \)-fold cross-validation to choose \( \alpha \). Divide the training observations into \( K \) folds. For each \( k=1,2,\dots,K \) we:</li> 
-
 <ul>
 
 <p><li> repeat steps 1 and 2 on all but the \( k \)-th fold of the training data.</li>
@@ -572,17 +603,16 @@ subtree corresponding to \( \alpha \).
 
 <p><li> Finally  we average the results for each value of \( \alpha \), and pick \( \alpha \) to minimize the average error.</li>
 </ul>
+<p>
 <p><li> Return the subtree from Step 2 that corresponds to the chosen value of \( \alpha \).</li> 
 </ol>
 </div>
 </section>
 
-
 <section>
-<h2 id="___sec16">A Classification Tree </h2>
+<h2 id="a-classification-tree">A Classification Tree </h2>
 
-<p>
-A classification tree is very similar to a regression tree, except
+<p>A classification tree is very similar to a regression tree, except
 that it is used to predict a qualitative response rather than a
 quantitative one. Recall that for a regression tree, the predicted
 response for an observation is given by the mean response of the
@@ -593,15 +623,14 @@ in the region to which it belongs. In interpreting the results of a
 classification tree, we are often interested not only in the class
 prediction corresponding to a particular terminal node region, but
 also in the class proportions among the training observations that
-fall into that region.
+fall into that region.  
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec17">Growing a classification tree </h2>
+<h2 id="growing-a-classification-tree">Growing a classification tree </h2>
 
-<p>
-The task of growing a
+<p>The task of growing a
 classification tree is quite similar to the task of growing a
 regression tree. Just as in the regression setting, we use recursive
 binary splitting to grow a classification tree. However, in the
@@ -611,29 +640,29 @@ error rate</b>. Since we plan to assign an observation in a given region
 to the most commonly occurring error rate class of training
 observations in that region, the classification error rate is simply
 the fraction of the training observations in that region that do not
-belong to the most common class.
+belong to the most common class. 
+</p>
 
-<p>
-When building a classification tree, either the Gini index or the
+<p>When building a classification tree, either the Gini index or the
 entropy are typically used to evaluate the quality of a particular
 split, since these two approaches are more sensitive to node purity
-than is the classification error rate.
+than is the classification error rate. 
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec18">Classification tree, how to split nodes </h2>
+<h2 id="classification-tree-how-to-split-nodes">Classification tree, how to split nodes </h2>
 
-<p>
-If our targets are the outcome of a classification process that takes
+<p>If our targets are the outcome of a classification process that takes
 for example \( k=1,2,\dots,K \) values, the only thing we need to think of
 is to set up the splitting criteria for each node.
+</p>
 
-<p>
-We define a PDF \( p_{mk} \) that represents the number of observations of
+<p>We define a PDF \( p_{mk} \) that represents the number of observations of
 a class \( k \) in a region \( R_m \) with \( N_m \) observations. We represent
 this likelihood function in terms of the proportion \( I(y_i=k) \) of
 observations of this class in the region \( R_m \) as
+</p>
 
 <p>&nbsp;<br>
 $$
@@ -641,33 +670,34 @@ p_{mk} = \frac{1}{N_m}\sum_{x_i\in R_m}I(y_i=k).
 $$
 <p>&nbsp;<br>
 
-<p>
-We let \( p_{mk} \) represent the majority class of observations in region
+<p>We let \( p_{mk} \) represent the majority class of observations in region
 \( m \). The three most common ways of splitting a node are given by
+</p>
 
 <ul>
 <p><li> Misclassification error</li> 
 </ul>
+<p>
 <p>&nbsp;<br>
 $$
 p_{mk} = \frac{1}{N_m}\sum_{x_i\in R_m}I(y_i\ne k) = 1-p_{mk}.
 $$
 <p>&nbsp;<br>
 
-
 <ul>
 <p><li> Gini index \( g \)</li>
 </ul>
+<p>
 <p>&nbsp;<br>
 $$
 g = \sum_{k=1}^K p_{mk}(1-p_{mk}).
 $$
 <p>&nbsp;<br>
 
-
 <ul>
 <p><li> Information entropy or just entropy \( s \)</li>
 </ul>
+<p>
 <p>&nbsp;<br>
 $$
 s = -\sum_{k=1}^K p_{mk}\log{p_{mk}}.
@@ -675,13 +705,16 @@ $$
 <p>&nbsp;<br>
 </section>
 
-
 <section>
-<h2 id="___sec19">Visualizing the Tree, Classification </h2>
-<p>
+<h2 id="visualizing-the-tree-classification">Visualizing the Tree, Classification </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">os</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">os</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_breast_cancer
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeClassifier
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
@@ -714,16 +747,32 @@ export_graphviz(
 )
 cmd = <span style="color: #CD5555">&#39;dot -Tpng DataFiles/cancer.dot -o DataFiles/cancer.png&#39;</span>
 os.system(cmd)
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec20">Visualizing the Tree, The Moons  </h2>
-<p>
+<h2 id="visualizing-the-tree-the-moons">Visualizing the Tree, The Moons  </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #228B22"># Common imports</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #228B22"># Common imports</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span>  train_test_split 
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeClassifier
@@ -747,41 +796,72 @@ export_graphviz(
 )
 cmd = <span style="color: #CD5555">&#39;dot -Tpng DataFiles/moons.dot -o DataFiles/moons.png&#39;</span>
 os.system(cmd)
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec21">Other ways of visualizing the trees </h2>
+<h2 id="other-ways-of-visualizing-the-trees">Other ways of visualizing the trees </h2>
 
-<p>
-<b>Scikit-Learn</b> has also another way to visualize the trees which is very useful, here with the Iris data.
+<p><b>Scikit-Learn</b> has also another way to visualize the trees which is very useful, here with the Iris data.</p>
 
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_iris
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_iris
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn</span> <span style="color: #8B008B; font-weight: bold">import</span> tree
 X, y = load_iris(return_X_y=<span style="color: #8B008B; font-weight: bold">True</span>)
 tree_clf = tree.DecisionTreeClassifier()
 tree_clf = tree_clf.fit(X, y)
 <span style="color: #228B22"># and then plot the tree</span>
 tree.plot_tree(tree_clf) 
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec22">Printing out as text </h2>
+<h2 id="printing-out-as-text">Printing out as text </h2>
 
-<p>
-Alternatively, the tree can also be exported in textual format with the function exporttext.
+<p>Alternatively, the tree can also be exported in textual format with the function exporttext.
 This method doesn&#8217;t require the installation of external libraries and is more compact:
+</p>
 
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_iris
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_iris
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeClassifier
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> export_text
 iris = load_iris()
@@ -789,97 +869,104 @@ decision_tree = DecisionTreeClassifier(random_state=<span style="color: #B452CD"
 decision_tree = decision_tree.fit(iris.data, iris.target)
 r = export_text(decision_tree, feature_names=iris[<span style="color: #CD5555">&#39;feature_names&#39;</span>])
 <span style="color: #658b00">print</span>(r)
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec23">Algorithms for Setting up Decision Trees </h2>
-
-<p>
-Two algorithms stand out in the set up of decision trees:
+<h2 id="algorithms-for-setting-up-decision-trees">Algorithms for Setting up Decision Trees </h2>
 
+<p>Two algorithms stand out in the set up of decision trees:</p>
 <ol>
 <p><li> The CART (Classification And Regression Tree) algorithm for both classification and regression</li>
 <p><li> The ID3 algorithm based on the computation of the information gain for classification</li>
 </ol>
 <p>
-
-We discuss both algorithms with applications here. The popular library
+<p>We discuss both algorithms with applications here. The popular library
 <b>Scikit-Learn</b> uses the CART algorithm. For classification problems
 you can use either the <b>gini</b> index or the <b>entropy</b> to split a tree
 in two branches.
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec24">The CART algorithm for Classification </h2>
+<h2 id="the-cart-algorithm-for-classification">The CART algorithm for Classification </h2>
 
-<p>
-For classification, the CART algorithm splits the data set in two subsets using a single feature \( k \) and a threshold \( t_k \).
+<p>For classification, the CART algorithm splits the data set in two subsets using a single feature \( k \) and a threshold \( t_k \).
 This could be for example a threshold set by a number below a certain circumference of a malign tumor.
+</p>
 
-<p>
-How do we find these two quantities?
+<p>How do we find these two quantities?
 We search for the pair \( (k,t_k) \) that produces the purest subset using for example the <b>gini</b> factor \( G \).
 The cost function it tries to minimize is then
+</p>
 <p>&nbsp;<br>
 $$
 C(k,t_k) = \frac{m_{\mathrm{left}}}{m}G_{\mathrm{left}}+ \frac{m_{\mathrm{right}}}{m}G_{\mathrm{right}},
 $$
 <p>&nbsp;<br>
 
-where \( G_{\mathrm{left/right}} \) measures the impurity of the left/right subset  and \( m_{\mathrm{left/right}} \)
+<p>where \( G_{\mathrm{left/right}} \) measures the impurity of the left/right subset  and \( m_{\mathrm{left/right}} \)
  is the number of instances in the left/right subset
+</p>
 
-<p>
-Once it has successfully split the training set in two, it splits the subsets using the same logic, then the subsubsets
+<p>Once it has successfully split the training set in two, it splits the subsets using the same logic, then the subsubsets
 and so on, recursively. It stops recursing once it reaches the maximum depth (defined by the
 \( max\_depth \) hyperparameter), or if it cannot find a split that will reduce impurity. A few other
 hyperparameters control additional stopping conditions such as the \( min\_samples\_split \),
 \( min\_samples\_leaf \), \( min\_weight\_fraction\_leaf \), and \( max\_leaf\_nodes \).
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec25">The CART algorithm for Regression </h2>
+<h2 id="the-cart-algorithm-for-regression">The CART algorithm for Regression </h2>
 
-<p>
-The CART algorithm for regression works is similar to the one for classification except that instead of trying to split the
+<p>The CART algorithm for regression works is similar to the one for classification except that instead of trying to split the
 training set in a way that minimizes say the <b>gini</b> or <b>entropy</b> impurity, it now tries to split the training set in a way that minimizes our well-known mean-squared error (MSE). The cost function is now
+</p>
 <p>&nbsp;<br>
 $$
 C(k,t_k) = \frac{m_{\mathrm{left}}}{m}\mathrm{MSE}_{\mathrm{left}}+ \frac{m_{\mathrm{right}}}{m}\mathrm{MSE}_{\mathrm{right}}.
 $$
 <p>&nbsp;<br>
 
-Here the MSE for a specific node is defined as
+<p>Here the MSE for a specific node is defined as</p>
 <p>&nbsp;<br>
 $$
 \mathrm{MSE}_{\mathrm{node}}=\frac{1}{m_\mathrm{node}}\sum_{i\in \mathrm{node}}(\overline{y}_{\mathrm{node}}-y_i)^2,
 $$
 <p>&nbsp;<br>
 
-with
+<p>with</p>
 <p>&nbsp;<br>
 $$
 \overline{y}_{\mathrm{node}}=\frac{1}{m_\mathrm{node}}\sum_{i\in \mathrm{node}}y_i,
 $$
 <p>&nbsp;<br>
 
-the mean value of all observations in a specific node.
+<p>the mean value of all observations in a specific node.</p>
 
-<p>
-Without any regularization, the regression task for decision trees, 
+<p>Without any regularization, the regression task for decision trees, 
 just like for classification tasks, is  prone to overfitting.
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec26">Computing the Gini index </h2>
+<h2 id="computing-the-gini-index">Computing the Gini index </h2>
 
-<p>
-The example we will look at is a classical one in many Machine
+<p>The example we will look at is a classical one in many Machine
 Learning applications. Based on various meteorological features, we
 have several so-called attributes which decide whether we at the end
 will do some outdoor activity like skiing, going for a bike ride etc
@@ -889,10 +976,10 @@ etc.  The table here contains the feautures <b>outlook</b>, <b>temperature</b>,
 attributes for each feature are then sunny, overcast and rain for the
 outlook, hot, cold and mild for temperature, high and normal for
 humidity and weak and strong for wind.
+</p>
 
-<p>
-The table here summarizes the various attributes and
-<table border="1">
+<p>The table here summarizes the various attributes and</p>
+<table class="dotable" border="1">
 <thead>
 <tr><th align="center">Day</th> <th align="center">Outlook </th> <th align="center">Temperature</th> <th align="center">Humidity</th> <th align="center"> Wind </th> <th align="center">Ride</th> </tr>
 </thead>
@@ -915,14 +1002,17 @@ The table here summarizes the various attributes and
 </table>
 </section>
 
-
 <section>
-<h2 id="___sec27">Simple Python Code to read in Data and perform Classification </h2>
+<h2 id="simple-python-code-to-read-in-data-and-perform-classification">Simple Python Code to read in Data and perform Classification </h2>
 
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #228B22"># Common imports</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #228B22"># Common imports</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">pandas</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">pd</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
@@ -989,26 +1079,41 @@ export_graphviz(
 )
 cmd = <span style="color: #CD5555">&#39;dot -Tpng DataFiles/cancer.dot -o DataFiles/cancer.png&#39;</span>
 os.system(cmd)
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec28">Computing the Gini Factor  </h2>
+<h2 id="computing-the-gini-factor">Computing the Gini Factor  </h2>
 
-<p>
-The above functions (gini, entropy and misclassification error) are
+<p>The above functions (gini, entropy and misclassification error) are
 important components of the so-called CART algorithm. We will discuss
 this algorithm below after we have discussed the information gain
 algorithm ID3.
+</p>
 
-<p>
-In the example here we have converted all our attributes into numerical values \( 0,1,2 \) etc.
+<p>In the example here we have converted all our attributes into numerical values \( 0,1,2 \) etc.</p>
 
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #228B22"># Split a dataset based on an attribute and an attribute value</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #228B22"># Split a dataset based on an attribute and an attribute value</span>
 <span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">test_split</span>(index, value, dataset):
 	left, right = <span style="color: #658b00">list</span>(), <span style="color: #658b00">list</span>()
 	<span style="color: #8B008B; font-weight: bold">for</span> row <span style="color: #8B008B">in</span> dataset:
@@ -1068,16 +1173,28 @@ dataset = [[<span style="color: #B452CD">0</span>,<span style="color: #B452CD">0
 
 split = get_split(dataset)
 <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Split: [X%d &lt; %.3f]&#39;</span> % ((split[<span style="color: #CD5555">&#39;index&#39;</span>]+<span style="color: #B452CD">1</span>), split[<span style="color: #CD5555">&#39;value&#39;</span>]))
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec29">Entropy and the ID3 algorithm </h2>
+<h2 id="entropy-and-the-id3-algorithm">Entropy and the ID3 algorithm </h2>
 
-<p>
-The ID3 algorithm  learns decision trees by constructing
+<p>The ID3 algorithm  learns decision trees by constructing
 them in a top down way, beginning with the question <b>which attribute should be tested at the root of the tree</b>?
+</p>
 
 <ol>
 <p><li> Each instance attribute is evaluated using a statistical test to determine how well it alone classifies the training examples.</li>
@@ -1088,33 +1205,35 @@ them in a top down way, beginning with the question <b>which attribute should be
 <p><li> This forms a greedy search for an acceptable decision tree, in which the algorithm never backtracks to reconsider earlier choices.</li> 
 </ol>
 <p>
-
-The ID3 algorithm selects which attribute to test at each node in the
+<p>The ID3 algorithm selects which attribute to test at each node in the
 tree.
+</p>
 
-<p>
-We would like to select the attribute that is most useful for classifying
+<p>We would like to select the attribute that is most useful for classifying
 examples.
+</p>
 
-<p>
-What is a good quantitative measure of the worth of an attribute?
+<p>What is a good quantitative measure of the worth of an attribute?</p>
 
-<p>
-Information gain measures how well a given attribute separates the
+<p>Information gain measures how well a given attribute separates the
 training examples according to their target classification.
+</p>
 
-<p>
-The ID3 algorithm uses this information gain measure to select among the candidate
+<p>The ID3 algorithm uses this information gain measure to select among the candidate
 attributes at each step while growing the tree.
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec30">Cancer Data again now with Decision Trees and other Methods </h2>
-<p>
+<h2 id="cancer-data-again-now-with-decision-trees-and-other-methods">Cancer Data again now with Decision Trees and other Methods </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span>  train_test_split 
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_breast_cancer
@@ -1155,16 +1274,32 @@ svm.fit(X_train_scaled, y_train)
 <span style="color: #228B22"># Decision Trees</span>
 deep_tree_clf.fit(X_train_scaled, y_train)
 <span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Test set accuracy with Decision Trees and scaled data: {:.2f}&quot;</span>.format(deep_tree_clf.score(X_test_scaled,y_test)))
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec31">Another example, the moons again </h2>
-<p>
+<h2 id="another-example-the-moons-again">Another example, the moons again </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">__future__</span> <span style="color: #8B008B; font-weight: bold">import</span> division, print_function, unicode_literals
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">__future__</span> <span style="color: #8B008B; font-weight: bold">import</span> division, print_function, unicode_literals
 
 <span style="color: #228B22"># Common imports</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
@@ -1228,16 +1363,32 @@ plt.subplot(<span style="color: #B452CD">122</span>)
 plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-<span style="color: #B452CD">1.5</span>, <span style="color: #B452CD">2.5</span>, -<span style="color: #B452CD">1</span>, <span style="color: #B452CD">1.5</span>], iris=<span style="color: #8B008B; font-weight: bold">False</span>)
 plt.title(<span style="color: #CD5555">&quot;min_samples_leaf = {}&quot;</span>.format(deep_tree_clf2.min_samples_leaf), fontsize=<span style="color: #B452CD">14</span>)
 plt.show()
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec32">Playing around with regions </h2>
-<p>
+<h2 id="playing-around-with-regions">Playing around with regions </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span>np.random.seed(<span style="color: #B452CD">6</span>)
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;">np.random.seed(<span style="color: #B452CD">6</span>)
 Xs = np.random.rand(<span style="color: #B452CD">100</span>, <span style="color: #B452CD">2</span>) - <span style="color: #B452CD">0.5</span>
 ys = (Xs[:, <span style="color: #B452CD">0</span>] &gt; <span style="color: #B452CD">0</span>).astype(np.float32) * <span style="color: #B452CD">2</span>
 
@@ -1257,39 +1408,86 @@ plt.subplot(<span style="color: #B452CD">122</span>)
 plot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-<span style="color: #B452CD">0.7</span>, <span style="color: #B452CD">0.7</span>, -<span style="color: #B452CD">0.7</span>, <span style="color: #B452CD">0.7</span>], iris=<span style="color: #8B008B; font-weight: bold">False</span>)
 
 plt.show()
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec33">Regression trees </h2>
-<p>
+<h2 id="regression-trees">Regression trees </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #228B22"># Quadratic training set + noise</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #228B22"># Quadratic training set + noise</span>
 np.random.seed(<span style="color: #B452CD">42</span>)
 m = <span style="color: #B452CD">200</span>
 X = np.random.rand(m, <span style="color: #B452CD">1</span>)
 y = <span style="color: #B452CD">4</span> * (X - <span style="color: #B452CD">0.5</span>) ** <span style="color: #B452CD">2</span>
 y = y + np.random.randn(m, <span style="color: #B452CD">1</span>) / <span style="color: #B452CD">10</span>
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeRegressor
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeRegressor
 
 tree_reg = DecisionTreeRegressor(max_depth=<span style="color: #B452CD">2</span>, random_state=<span style="color: #B452CD">42</span>)
 tree_reg.fit(X, y)
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec34">Final regressor code </h2>
-<p>
+<h2 id="final-regressor-code">Final regressor code </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeRegressor
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeRegressor
 
 tree_reg1 = DecisionTreeRegressor(random_state=<span style="color: #B452CD">42</span>, max_depth=<span style="color: #B452CD">2</span>)
 tree_reg2 = DecisionTreeRegressor(random_state=<span style="color: #B452CD">42</span>, max_depth=<span style="color: #B452CD">3</span>)
@@ -1327,11 +1525,26 @@ plt.text(<span style="color: #B452CD">0.3</span>, <span style="color: #B452CD">0
 plt.title(<span style="color: #CD5555">&quot;max_depth=3&quot;</span>, fontsize=<span style="color: #B452CD">14</span>)
 
 plt.show()
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span>tree_reg1 = DecisionTreeRegressor(random_state=<span style="color: #B452CD">42</span>)
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;">tree_reg1 = DecisionTreeRegressor(random_state=<span style="color: #B452CD">42</span>)
 tree_reg2 = DecisionTreeRegressor(random_state=<span style="color: #B452CD">42</span>, min_samples_leaf=<span style="color: #B452CD">10</span>)
 tree_reg1.fit(X, y)
 tree_reg2.fit(X, y)
@@ -1359,12 +1572,24 @@ plt.xlabel(<span style="color: #CD5555">&quot;$x_1$&quot;</span>, fontsize=<span
 plt.title(<span style="color: #CD5555">&quot;min_samples_leaf={}&quot;</span>.format(tree_reg2.min_samples_leaf), fontsize=<span style="color: #B452CD">14</span>)
 
 plt.show()
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec35">Pros and cons of trees, pros </h2>
+<h2 id="pros-and-cons-of-trees-pros">Pros and cons of trees, pros </h2>
 
 <ul>
 <p><li> White box, easy to interpret model. Some people believe that decision trees more closely mirror human decision-making than do the regression and classification approaches discussed earlier (think of support vector machines)</li>
@@ -1377,9 +1602,8 @@ plt.show()
 </ul>
 </section>
 
-
 <section>
-<h2 id="___sec36">Disadvantages </h2>
+<h2 id="disadvantages">Disadvantages </h2>
 
 <ul>
 <p><li> Unfortunately, trees generally do not have the same level of predictive accuracy as some of the other regression and classification approaches</li>
@@ -1391,28 +1615,27 @@ plt.show()
 <p><li> Features with many levels may be preferred over features with less levels since for them it is <em>more easy</em> to split the dataset such that the sub datasets only contain pure target feature values. This issue can be addressed by preferring for instance the information gain ratio as splitting criteria over information gain</li>
 </ul>
 <p>
-
-However, by aggregating many decision trees, using methods like
+<p>However, by aggregating many decision trees, using methods like
 bagging, random forests, and boosting, the predictive performance of
 trees can be substantially improved.
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec37">Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods </h2>
+<h2 id="ensemble-methods-from-a-single-tree-to-many-trees-and-extreme-boosting-meet-the-jungle-of-methods">Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods </h2>
 
-<p>
-As stated above and seen in many of the examples discussed here about
+<p>As stated above and seen in many of the examples discussed here about
 a single decision tree, we often end up overfitting our training
 data. This normally means that we have a high variance. Can we reduce
 the variance of a statistical learning method?
+</p>
 
-<p>
-This leads us to a set of different methods that can combine different
+<p>This leads us to a set of different methods that can combine different
 machine learning algorithms or just use one of them to construct
 forests and jungles of trees, homogeneous ones or heterogenous
 ones. These methods are recognized by different names which we will
 try to explain here. These are
+</p>
 
 <ol>
 <p><li> Voting classifiers</li>
@@ -1421,50 +1644,48 @@ try to explain here. These are
 <p><li> Boosting methods, from adaptive to Extreme Gradient Boosting (XGBoost)</li>
 </ol>
 <p>
-
-We discuss these methods here.
+<p>We discuss these methods here.</p>
 </section>
 
-
 <section>
-<h2 id="___sec38">An Overview of Ensemble Methods  </h2>
+<h2 id="an-overview-of-ensemble-methods">An Overview of Ensemble Methods  </h2>
 
-<p>
-<br /><br /><center><p><img src="DataFiles/ensembleoverview.png" align="bottom" width=600></p></center><br /><br />
+<br/><br/>
+<center>
+<p><img src="DataFiles/ensembleoverview.png" width="600" align="bottom"></p>
+</center>
+<br/><br/>
 </section>
 
-
 <section>
-<h2 id="___sec39">Bagging </h2>
+<h2 id="bagging">Bagging </h2>
 
-<p>
-The <b>plain</b> decision trees suffer from high
+<p>The <b>plain</b> decision trees suffer from high
 variance. This means that if we split the training data into two parts
 at random, and fit a decision tree to both halves, the results that we
 get could be quite different. In contrast, a procedure with low
 variance will yield similar results if applied repeatedly to distinct
 data sets; linear regression tends to have low variance, if the ratio
-of \( n \) to \( p \) is moderately large.
+of \( n \) to \( p \) is moderately large. 
+</p>
 
-<p>
-<b>Bootstrap aggregation</b>, or just <b>bagging</b>, is a
+<p><b>Bootstrap aggregation</b>, or just <b>bagging</b>, is a
 general-purpose procedure for reducing the variance of a statistical
-learning method.
+learning method. 
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec40">More bagging </h2>
+<h2 id="more-bagging">More bagging </h2>
 
-<p>
-Bagging typically results in improved accuracy
+<p>Bagging typically results in improved accuracy
 over prediction using a single tree. Unfortunately, however, it can be
 difficult to interpret the resulting model. Recall that one of the
 advantages of decision trees is the attractive and easily interpreted
 diagram that results.
+</p>
 
-<p>
-However, when we bag a large number of trees, it is no longer
+<p>However, when we bag a large number of trees, it is no longer
 possible to represent the resulting statistical learning procedure
 using a single tree, and it is no longer clear which variables are
 most important to the procedure. Thus, bagging improves prediction
@@ -1479,15 +1700,19 @@ trees. A large value indicates an important predictor. Similarly, in
 the context of bagging classification trees, we can add up the total
 amount that the Gini index  is decreased by splits over a given
 predictor, averaged over all \( B \) trees.
+</p>
 </section>
 
-
 <section>
-<h2 id="___sec41">Simple Voting Example, head or tail </h2>
-<p>
+<h2 id="simple-voting-example-head-or-tail">Simple Voting Example, head or tail </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span>heads_proba = <span style="color: #B452CD">0.51</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;">heads_proba = <span style="color: #B452CD">0.51</span>
 coin_tosses = (np.random.rand(<span style="color: #B452CD">10000</span>, <span style="color: #B452CD">10</span>) &lt; heads_proba).astype(np.int32)
 cumulative_heads_ratio = np.cumsum(coin_tosses, axis=<span style="color: #B452CD">0</span>) / np.arange(<span style="color: #B452CD">1</span>, <span style="color: #B452CD">10001</span>).reshape(-<span style="color: #B452CD">1</span>, <span style="color: #B452CD">1</span>)
 plt.figure(figsize=(<span style="color: #B452CD">8</span>,<span style="color: #B452CD">3.5</span>))
@@ -1500,16 +1725,32 @@ plt.legend(loc=<span style="color: #CD5555">&quot;lower right&quot;</span>)
 plt.axis([<span style="color: #B452CD">0</span>, <span style="color: #B452CD">10000</span>, <span style="color: #B452CD">0.42</span>, <span style="color: #B452CD">0.58</span>])
 save_fig(<span style="color: #CD5555">&quot;votingsimple&quot;</span>)
 plt.show()
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec42">Using the Voting Classifier </h2>
-<p>
+<h2 id="using-the-voting-classifier">Using the Voting Classifier </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> make_moons
 
 X, y = make_moons(n_samples=<span style="color: #B452CD">500</span>, noise=<span style="color: #B452CD">0.30</span>, random_state=<span style="color: #B452CD">42</span>)
@@ -1552,17 +1793,33 @@ voting_clf.fit(X_train, y_train)
     clf.fit(X_train, y_train)
     y_pred = clf.predict(X_test)
     <span style="color: #658b00">print</span>(clf.<span style="color: #00688B">__class__</span>.<span style="color: #00688B">__name__</span>, accuracy_score(y_test, y_pred))
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec43">Please, not the moons again! Voting and Bagging </h2>
+<h2 id="please-not-the-moons-again-voting-and-bagging">Please, not the moons again! Voting and Bagging </h2>
 
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> make_moons
 
 X, y = make_moons(n_samples=<span style="color: #B452CD">500</span>, noise=<span style="color: #B452CD">0.30</span>, random_state=<span style="color: #B452CD">42</span>)
@@ -1580,21 +1837,51 @@ voting_clf = VotingClassifier(
     estimators=[(<span style="color: #CD5555">&#39;lr&#39;</span>, log_clf), (<span style="color: #CD5555">&#39;rf&#39;</span>, rnd_clf), (<span style="color: #CD5555">&#39;svc&#39;</span>, svm_clf)],
     voting=<span style="color: #CD5555">&#39;hard&#39;</span>)
 voting_clf.fit(X_train, y_train)
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> accuracy_score
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> accuracy_score
 
 <span style="color: #8B008B; font-weight: bold">for</span> clf <span style="color: #8B008B">in</span> (log_clf, rnd_clf, svm_clf, voting_clf):
     clf.fit(X_train, y_train)
     y_pred = clf.predict(X_test)
     <span style="color: #658b00">print</span>(clf.<span style="color: #00688B">__class__</span>.<span style="color: #00688B">__name__</span>, accuracy_score(y_test, y_pred))
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span>log_clf = LogisticRegression(random_state=<span style="color: #B452CD">42</span>)
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;">log_clf = LogisticRegression(random_state=<span style="color: #B452CD">42</span>)
 rnd_clf = RandomForestClassifier(random_state=<span style="color: #B452CD">42</span>)
 svm_clf = SVC(probability=<span style="color: #8B008B; font-weight: bold">True</span>, random_state=<span style="color: #B452CD">42</span>)
 
@@ -1602,27 +1889,58 @@ voting_clf = VotingClassifier(
     estimators=[(<span style="color: #CD5555">&#39;lr&#39;</span>, log_clf), (<span style="color: #CD5555">&#39;rf&#39;</span>, rnd_clf), (<span style="color: #CD5555">&#39;svc&#39;</span>, svm_clf)],
     voting=<span style="color: #CD5555">&#39;soft&#39;</span>)
 voting_clf.fit(X_train, y_train)
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> accuracy_score
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> accuracy_score
 
 <span style="color: #8B008B; font-weight: bold">for</span> clf <span style="color: #8B008B">in</span> (log_clf, rnd_clf, svm_clf, voting_clf):
     clf.fit(X_train, y_train)
     y_pred = clf.predict(X_test)
     <span style="color: #658b00">print</span>(clf.<span style="color: #00688B">__class__</span>.<span style="color: #00688B">__name__</span>, accuracy_score(y_test, y_pred))
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec44">Bagging Examples  </h2>
+<h2 id="bagging-examples">Bagging Examples  </h2>
 
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.ensemble</span> <span style="color: #8B008B; font-weight: bold">import</span> BaggingClassifier
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.ensemble</span> <span style="color: #8B008B; font-weight: bold">import</span> BaggingClassifier
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeClassifier
 
 bag_clf = BaggingClassifier(
@@ -1630,25 +1948,70 @@ bag_clf = BaggingClassifier(
     max_samples=<span style="color: #B452CD">100</span>, bootstrap=<span style="color: #8B008B; font-weight: bold">True</span>, n_jobs=-<span style="color: #B452CD">1</span>, random_state=<span style="color: #B452CD">42</span>)
 bag_clf.fit(X_train, y_train)
 y_pred = bag_clf.predict(X_test)
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> accuracy_score
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> accuracy_score
 <span style="color: #658b00">print</span>(accuracy_score(y_test, y_pred))
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span>tree_clf = DecisionTreeClassifier(random_state=<span style="color: #B452CD">42</span>)
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;">tree_clf = DecisionTreeClassifier(random_state=<span style="color: #B452CD">42</span>)
 tree_clf.fit(X_train, y_train)
 y_pred_tree = tree_clf.predict(X_test)
 <span style="color: #658b00">print</span>(accuracy_score(y_test, y_pred_tree))
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">matplotlib.colors</span> <span style="color: #8B008B; font-weight: bold">import</span> ListedColormap
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">matplotlib.colors</span> <span style="color: #8B008B; font-weight: bold">import</span> ListedColormap
 
 <span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">plot_decision_boundary</span>(clf, X, y, axes=[-<span style="color: #B452CD">1.5</span>, <span style="color: #B452CD">2.5</span>, -<span style="color: #B452CD">1</span>, <span style="color: #B452CD">1.5</span>], alpha=<span style="color: #B452CD">0.5</span>, contour=<span style="color: #8B008B; font-weight: bold">True</span>):
     x1s = np.linspace(axes[<span style="color: #B452CD">0</span>], axes[<span style="color: #B452CD">1</span>], <span style="color: #B452CD">100</span>)
@@ -1675,20 +2038,36 @@ plot_decision_boundary(bag_clf, X, y)
 plt.title(<span style="color: #CD5555">&quot;Decision Trees with Bagging&quot;</span>, fontsize=<span style="color: #B452CD">14</span>)
 save_fig(<span style="color: #CD5555">&quot;baggingtree&quot;</span>)
 plt.show()
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
-
 <section>
-<h2 id="___sec45">Making your own Bootstrap: Changing the Level of the Decision Tree </h2>
+<h2 id="making-your-own-bootstrap-changing-the-level-of-the-decision-tree">Making your own Bootstrap: Changing the Level of the Decision Tree </h2>
 
-<p>
-Let us bring up our good old boostrap example from the linear regression lectures. We change the linerar regression algorithm with
+<p>Let us bring up our good old boostrap example from the linear regression lectures. We change the linerar regression algorithm with
 a decision tree wth different depths and perform a bootstrap aggregate (in this case we perform as many bootstraps as data points \( n \)).
-<p>
+</p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.pipeline</span> <span style="color: #8B008B; font-weight: bold">import</span> make_pipeline
@@ -1745,7 +2124,20 @@ plt.plot(polydegree, variance, label=<span style="color: #CD5555">&#39;Variance&
 plt.legend()
 save_fig(<span style="color: #CD5555">&quot;baggingboot&quot;</span>)
 plt.show()
-</pre></div>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 </section>
 
 
@@ -1761,124 +2153,124 @@ plt.show()
 // https://github.com/hakimel/reveal.js#configuration
 Reveal.initialize({
 
-    // Display navigation controls in the bottom right corner
-    controls: true,
+  // Display navigation controls in the bottom right corner
+  controls: true,
 
-    // Display progress bar (below the horiz. slider)
-    progress: true,
+  // Display progress bar (below the horiz. slider)
+  progress: true,
 
-    // Display the page number of the current slide
-    slideNumber: true,
+  // Display the page number of the current slide
+  slideNumber: true,
 
-    // Push each slide change to the browser history
-    history: false,
+  // Push each slide change to the browser history
+  history: false,
 
-    // Enable keyboard shortcuts for navigation
-    keyboard: true,
+  // Enable keyboard shortcuts for navigation
+  keyboard: true,
 
-    // Enable the slide overview mode
-    overview: true,
+  // Enable the slide overview mode
+  overview: true,
 
-    // Vertical centering of slides
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diff --git a/doc/pub/week44/html/week44-solarized.html b/doc/pub/week44/html/week44-solarized.html
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 <!-- tocinfo
 {'highest level': 2,
- 'sections': [('Overview of week 44', 2, None, '___sec0'),
-              ('Thursday', 2, None, '___sec1'),
-              ('Decision trees, overarching aims', 2, None, '___sec2'),
-              ('Basics of a tree', 2, None, '___sec3'),
-              ('A Sketch of a Tree, Regression problem', 2, None, '___sec4'),
+ 'sections': [('Overview of week 44', 2, None, 'overview-of-week-44'),
+              ('Thursday', 2, None, 'thursday'),
+              ('Decision trees, overarching aims',
+               2,
+               None,
+               'decision-trees-overarching-aims'),
+              ('Basics of a tree', 2, None, 'basics-of-a-tree'),
+              ('A Sketch of a Tree, Regression problem',
+               2,
+               None,
+               'a-sketch-of-a-tree-regression-problem'),
               ('A Sketch of a Tree, Classification  problem',
                2,
                None,
-               '___sec5'),
+               'a-sketch-of-a-tree-classification-problem'),
               ('A typical Decision Tree with its pertinent Jargon, '
                'Classification Problem',
                2,
                None,
-               '___sec6'),
-              ('General Features', 2, None, '___sec7'),
-              ('How do we set it up?', 2, None, '___sec8'),
-              ('Decision trees and Regression', 2, None, '___sec9'),
-              ('Building a tree, regression', 2, None, '___sec10'),
+               'a-typical-decision-tree-with-its-pertinent-jargon-classification-problem'),
+              ('General Features', 2, None, 'general-features'),
+              ('How do we set it up?', 2, None, 'how-do-we-set-it-up'),
+              ('Decision trees and Regression',
+               2,
+               None,
+               'decision-trees-and-regression'),
+              ('Building a tree, regression',
+               2,
+               None,
+               'building-a-tree-regression'),
               ('A top-down approach, recursive binary splitting',
                2,
                None,
-               '___sec11'),
-              ('Making a tree', 2, None, '___sec12'),
-              ('Pruning the tree', 2, None, '___sec13'),
-              ('Cost complexity pruning', 2, None, '___sec14'),
-              ('Schematic Regression Procedure', 2, None, '___sec15'),
-              ('A Classification Tree', 2, None, '___sec16'),
-              ('Growing a classification tree', 2, None, '___sec17'),
-              ('Classification tree, how to split nodes', 2, None, '___sec18'),
-              ('Visualizing the Tree, Classification', 2, None, '___sec19'),
-              ('Visualizing the Tree, The Moons', 2, None, '___sec20'),
-              ('Other ways of visualizing the trees', 2, None, '___sec21'),
-              ('Printing out as text', 2, None, '___sec22'),
-              ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
-              ('The CART algorithm for Classification', 2, None, '___sec24'),
-              ('The CART algorithm for Regression', 2, None, '___sec25'),
-              ('Computing the Gini index', 2, None, '___sec26'),
+               'a-top-down-approach-recursive-binary-splitting'),
+              ('Making a tree', 2, None, 'making-a-tree'),
+              ('Pruning the tree', 2, None, 'pruning-the-tree'),
+              ('Cost complexity pruning', 2, None, 'cost-complexity-pruning'),
+              ('Schematic Regression Procedure',
+               2,
+               None,
+               'schematic-regression-procedure'),
+              ('A Classification Tree', 2, None, 'a-classification-tree'),
+              ('Growing a classification tree',
+               2,
+               None,
+               'growing-a-classification-tree'),
+              ('Classification tree, how to split nodes',
+               2,
+               None,
+               'classification-tree-how-to-split-nodes'),
+              ('Visualizing the Tree, Classification',
+               2,
+               None,
+               'visualizing-the-tree-classification'),
+              ('Visualizing the Tree, The Moons',
+               2,
+               None,
+               'visualizing-the-tree-the-moons'),
+              ('Other ways of visualizing the trees',
+               2,
+               None,
+               'other-ways-of-visualizing-the-trees'),
+              ('Printing out as text', 2, None, 'printing-out-as-text'),
+              ('Algorithms for Setting up Decision Trees',
+               2,
+               None,
+               'algorithms-for-setting-up-decision-trees'),
+              ('The CART algorithm for Classification',
+               2,
+               None,
+               'the-cart-algorithm-for-classification'),
+              ('The CART algorithm for Regression',
+               2,
+               None,
+               'the-cart-algorithm-for-regression'),
+              ('Computing the Gini index', 2, None, 'computing-the-gini-index'),
               ('Simple Python Code to read in Data and perform Classification',
                2,
                None,
-               '___sec27'),
-              ('Computing the Gini Factor', 2, None, '___sec28'),
-              ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
+               'simple-python-code-to-read-in-data-and-perform-classification'),
+              ('Computing the Gini Factor',
+               2,
+               None,
+               'computing-the-gini-factor'),
+              ('Entropy and the ID3 algorithm',
+               2,
+               None,
+               'entropy-and-the-id3-algorithm'),
               ('Cancer Data again now with Decision Trees and other Methods',
                2,
                None,
-               '___sec30'),
-              ('Another example, the moons again', 2, None, '___sec31'),
-              ('Playing around with regions', 2, None, '___sec32'),
-              ('Regression trees', 2, None, '___sec33'),
-              ('Final regressor code', 2, None, '___sec34'),
-              ('Pros and cons of trees, pros', 2, None, '___sec35'),
-              ('Disadvantages', 2, None, '___sec36'),
+               'cancer-data-again-now-with-decision-trees-and-other-methods'),
+              ('Another example, the moons again',
+               2,
+               None,
+               'another-example-the-moons-again'),
+              ('Playing around with regions',
+               2,
+               None,
+               'playing-around-with-regions'),
+              ('Regression trees', 2, None, 'regression-trees'),
+              ('Final regressor code', 2, None, 'final-regressor-code'),
+              ('Pros and cons of trees, pros',
+               2,
+               None,
+               'pros-and-cons-of-trees-pros'),
+              ('Disadvantages', 2, None, 'disadvantages'),
               ('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
                'Boosting, Meet the Jungle of Methods',
                2,
                None,
-               '___sec37'),
-              ('An Overview of Ensemble Methods', 2, None, '___sec38'),
-              ('Bagging', 2, None, '___sec39'),
-              ('More bagging', 2, None, '___sec40'),
-              ('Simple Voting Example, head or tail', 2, None, '___sec41'),
-              ('Using the Voting Classifier', 2, None, '___sec42'),
+               'ensemble-methods-from-a-single-tree-to-many-trees-and-extreme-boosting-meet-the-jungle-of-methods'),
+              ('An Overview of Ensemble Methods',
+               2,
+               None,
+               'an-overview-of-ensemble-methods'),
+              ('Bagging', 2, None, 'bagging'),
+              ('More bagging', 2, None, 'more-bagging'),
+              ('Simple Voting Example, head or tail',
+               2,
+               None,
+               'simple-voting-example-head-or-tail'),
+              ('Using the Voting Classifier',
+               2,
+               None,
+               'using-the-voting-classifier'),
               ('Please, not the moons again! Voting and Bagging',
                2,
                None,
-               '___sec43'),
-              ('Bagging Examples', 2, None, '___sec44'),
+               'please-not-the-moons-again-voting-and-bagging'),
+              ('Bagging Examples', 2, None, 'bagging-examples'),
               ('Making your own Bootstrap: Changing the Level of the Decision '
                'Tree',
                2,
                None,
-               '___sec45')]}
+               'making-your-own-bootstrap-changing-the-level-of-the-decision-tree')]}
 end of tocinfo -->
 
 <body>
@@ -153,131 +218,110 @@ MathJax.Hub.Config({
 </script>
 
 
-
-    
 <!-- ------------------- main content ---------------------- -->
+<center>
+<h1>Week 44: From Decision Trees to Bagging methods</h1>
+</center>  <!-- document title -->
 
-
-
-<center><h1>Week 44: From Decision Trees to Bagging methods</h1></center>  <!-- document title -->
-
-<p>
 <!-- author(s): Morten Hjorth-Jensen -->
-
 <center>
 <b>Morten Hjorth-Jensen</b> [1, 2]
 </center>
-
-<p>
 <!-- institution(s) -->
+<center>
+[1] <b>Department of Physics, University of Oslo</b>
+</center>
+<center>
+[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b>
+</center>
+<br>
+<center>
+<h4>Nov 1, 2021</h4>
+</center> <!-- date -->
+<br>
 
-<center>[1] <b>Department of Physics, University of Oslo</b></center>
-<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
-<br>
-<p>
-<center><h4>Nov 5, 2020</h4></center> <!-- date -->
-<br>
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
-<h2 id="___sec0">Overview of week 44 </h2>
+<h2 id="overview-of-week-44">Overview of week 44 </h2>
 
 <ul>
-<li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober29.mp4?vrtx=view-as-webpage" target="_blank">Thursday: Wrapping up PCA from last week and basics of decision trees, classification and regression algorithms with video of lecture</a></li> 
-<li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober30.mp4?vrtx=view-as-webpage" target="_blank">Friday: Decision trees, voting models and bagging with video of lecture</a></li> 
+<li> Thursday: Wrapping up PCA from last week and basics of decision trees, classification and regression algorithms</li>
+<li> Friday: Decision trees, voting models and bagging</li>
 </ul>
+<p>Geron's chapter 6 covers decision trees while ensemble models, voting and bagging are discussed in chapter 7. See also lecture from <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf" target="_blank">STK-IN4300, lecture 7</a>. Chapter 9.2 of Hastie et al contains also a good discussion.</p>
 
-Geron's chapter 6 covers decision trees while ensemble models, voting and bagging are discussed in chapter 7. See also lecture from <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf" target="_blank">STK-IN4300, lecture 7</a>. Chapter 9.2 of Hastie et al contains also a good discussion.
-
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="thursday">Thursday </h2>
 
-<h2 id="___sec1">Thursday </h2>
+<p>Decision trees and wrapping up PCA.</p>
 
-<p>
-Decision trees and wrapping up PCA.
-
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="decision-trees-overarching-aims">Decision trees, overarching aims  </h2>
 
-<h2 id="___sec2">Decision trees, overarching aims  </h2>
-
-<p>
-We start here with the most basic algorithm, the so-called decision
+<p>We start here with the most basic algorithm, the so-called decision
 tree. With this basic algorithm we can in turn build more complex
 networks, spanning from homogeneous and heterogenous forests (bagging,
 random forests and more) to one of the most popular supervised
 algorithms nowadays, the extreme gradient boosting, or just
 XGBoost. But let us start with the simplest possible ingredient.
+</p>
 
-<p>
-Decision trees are supervised learning algorithms used for both,
+<p>Decision trees are supervised learning algorithms used for both,
 classification and regression tasks.
+</p>
 
-<p>
-The main idea of decision trees
+<p>The main idea of decision trees
 is to find those descriptive features which contain the most
 <b>information</b> regarding the target feature and then split the dataset
 along the values of these features such that the target feature values
 for the resulting underlying datasets are as pure as possible.
+</p>
 
-<p>
-The descriptive features which reproduce best the target/output features are normally  said
+<p>The descriptive features which reproduce best the target/output features are normally  said
 to be the most informative ones. The process of finding the <b>most
 informative</b> feature is done until we accomplish a stopping criteria
-where we then finally end up in so called <b>leaf nodes</b>.
+where we then finally end up in so called <b>leaf nodes</b>. 
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="basics-of-a-tree">Basics of a tree </h2>
 
-<h2 id="___sec3">Basics of a tree </h2>
-
-<p>
-A decision tree is typically divided into a <b>root node</b>, the <b>interior nodes</b>,
+<p>A decision tree is typically divided into a <b>root node</b>, the <b>interior nodes</b>,
 and the final <b>leaf nodes</b> or just <b>leaves</b>. These entities are then connected by so-called <b>branches</b>.
+</p>
 
-<p>
-The leaf nodes
+<p>The leaf nodes
 contain the predictions we will make for new query instances presented
 to our trained model. This is possible since the model has 
 learned the underlying structure of the training data and hence can,
 given some assumptions, make predictions about the target feature value
 (class) of unseen query instances.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="a-sketch-of-a-tree-regression-problem">A Sketch of a Tree, Regression problem  </h2>
 
-<h2 id="___sec4">A Sketch of a Tree, Regression problem  </h2>
-
-<p>
 <!-- FIGURE: [DataFiles/Regsimpletree.png, width=600 frac=0.8] -->
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="a-sketch-of-a-tree-classification-problem">A Sketch of a Tree, Classification  problem  </h2>
 
-<h2 id="___sec5">A Sketch of a Tree, Classification  problem  </h2>
-
-<p>
 <!-- FIGURE: [DataFiles/Classimpletree.png, width=600 frac=0.8] -->
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="a-typical-decision-tree-with-its-pertinent-jargon-classification-problem">A typical Decision Tree with its pertinent Jargon, Classification Problem </h2>
 
-<h2 id="___sec6">A typical Decision Tree with its pertinent Jargon, Classification Problem </h2>
+<br/><br/>
+<center>
+<p><img src="DataFiles/cancer.png" width="600" align="bottom"></p>
+</center>
+<br/><br/>
 
-<p>
-<br /><br /><center><p><img src="DataFiles/cancer.png" align="bottom" width=600></p></center><br /><br />
+<p>This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using <b>Scikit-Learn</b>'s decision tree classifier. Here we have used the so-called <b>gini</b> index (see below) to split the various branches.</p>
 
-<p>
-This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using <b>Scikit-Learn</b>'s decision tree classifier. Here we have used the so-called <b>gini</b> index (see below) to split the various branches.
-
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="general-features">General Features </h2>
 
-<h2 id="___sec7">General Features </h2>
-
-<p>
-The overarching approach to decision trees is a top-down approach.
+<p>The overarching approach to decision trees is a top-down approach.</p>
 
 <ul>
 <li> A leaf provides the classification of a given instance.</li>
@@ -285,18 +329,16 @@ The overarching approach to decision trees is a top-down approach.
 <li> A branch corresponds to a possible values of an attribute.</li>
 <li> An instance is classified by starting at the root node of the tree, testing the attribute specified by this node, then moving down the tree branch corresponding to the value of the attribute in the given example.</li>
 </ul>
-
-This process is then repeated for the subtree rooted at the new
+<p>This process is then repeated for the subtree rooted at the new
 node.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="how-do-we-set-it-up">How do we set it up? </h2>
 
-<h2 id="___sec8">How do we set it up? </h2>
-
-<p>
-In simplified terms, the process of training a decision tree and
+<p>In simplified terms, the process of training a decision tree and
 predicting the target features of query instances is as follows:
+</p>
 
 <ol>
 <li> Present a dataset containing of a number of training instances characterized by a number of descriptive features and a target feature</li>
@@ -304,17 +346,18 @@ predicting the target features of query instances is as follows:
 <li> Grow the tree until we accomplish a stopping criteria create leaf nodes which represent the <em>predictions</em> we want to make for new query instances</li>
 <li> Show query instances to the tree and run down the tree until we arrive at leaf nodes</li>
 </ol>
+<p>Then we are essentially done!</p>
 
-Then we are essentially done!
-
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
-<h2 id="___sec9">Decision trees and Regression  </h2>
-<p>
+<h2 id="decision-trees-and-regression">Decision trees and Regression  </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> PolynomialFeatures
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.linear_model</span> <span style="color: #8B008B; font-weight: bold">import</span> LinearRegression
@@ -352,7 +395,7 @@ c=lin_reg.intercept_
 <span style="color: #658b00">print</span> (<span style="color: #CD5555">&quot;first power: &quot;</span>, b[<span style="color: #B452CD">0</span>])
 <span style="color: #658b00">print</span> (<span style="color: #CD5555">&quot;second power: &quot;</span>,b[<span style="color: #B452CD">1</span>])
 
-z = np.arange(<span style="color: #B452CD">0</span>, steps, .<span style="color: #B452CD">01</span>)
+z = np.arange(<span style="color: #B452CD">0</span>, steps, <span style="color: #B452CD">.01</span>)
 z_mod=b[<span style="color: #B452CD">1</span>]*z**<span style="color: #B452CD">2</span>+b[<span style="color: #B452CD">0</span>]*z+c
 
 fit_mod=b[<span style="color: #B452CD">1</span>]*X**<span style="color: #B452CD">2</span>+b[<span style="color: #B452CD">0</span>]*X+c
@@ -402,96 +445,102 @@ plt.ylabel(<span style="color: #CD5555">&quot;Darget&quot;</span>)
 plt.title(<span style="color: #CD5555">&quot;Decision Tree Regression&quot;</span>)
 plt.legend()
 plt.show()
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="building-a-tree-regression">Building a tree, regression </h2>
 
-<h2 id="___sec10">Building a tree, regression </h2>
-
-<p>
-There are mainly two steps
-
+<p>There are mainly two steps</p>
 <ol>
 <li> We split the predictor space (the set of possible values \( x_1,x_2,\dots, x_p \)) into \( J \) distinct and non-non-overlapping regions, \( R_1,R_2,\dots,R_J \).</li>  
 <li> For every observation that falls into the region \( R_j \) , we make the same prediction, which is simply the mean of the response values for the training observations in \( R_j \).</li>
 </ol>
-
-How do we construct the regions \( R_1,\dots,R_J \)?  In theory, the
+<p>How do we construct the regions \( R_1,\dots,R_J \)?  In theory, the
 regions could have any shape. However, we choose to divide the
 predictor space into high-dimensional rectangles, or boxes, for
 simplicity and for ease of interpretation of the resulting predictive
 model. The goal is to find boxes \( R_1,\dots,R_J \) that minimize the
 MSE, given by
+</p>
 
 $$
 \sum_{j=1}^J\sum_{i\in R_j}(y_i-\overline{y}_{R_j})^2,
 $$
 
-<p>
-where \( \overline{y}_{R_j} \)  is the mean response for the training observations 
-within box \( j \).
+<p>where \( \overline{y}_{R_j} \)  is the mean response for the training observations 
+within box \( j \). 
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="a-top-down-approach-recursive-binary-splitting">A top-down approach, recursive binary splitting </h2>
 
-<h2 id="___sec11">A top-down approach, recursive binary splitting </h2>
-
-<p>
-Unfortunately, it is computationally infeasible to consider every
+<p>Unfortunately, it is computationally infeasible to consider every
 possible partition of the feature space into \( J \) boxes.  The common
 strategy is to take a top-down approach
+</p>
 
-<p>
-The approach is top-down because it begins at the top of the tree (all
+<p>The approach is top-down because it begins at the top of the tree (all
 observations belong to a single region) and then successively splits
 the predictor space; each split is indicated via two new branches
 further down on the tree. It is greedy because at each step of the
 tree-building process, the best split is made at that particular step,
 rather than looking ahead and picking a split that will lead to a
 better tree in some future step.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="making-a-tree">Making a tree </h2>
 
-<h2 id="___sec12">Making a tree </h2>
-
-<p>
-In order to implement the recursive binary splitting we start by selecting
+<p>In order to implement the recursive binary splitting we start by selecting
 the predictor \( x_j \) and a cutpoint \( s \) that splits the predictor space into two regions \( R_1 \) and \( R_2 \)
+</p>
 $$
 \left\{X\vert x_j < s\right\},
 $$
 
-and
+<p>and</p>
 $$
 \left\{X\vert x_j \geq s\right\},
 $$
 
-so that we obtain the lowest MSE, that is
+<p>so that we obtain the lowest MSE, that is</p>
 $$
 \sum_{i:x_i\in R_j}(y_i-\overline{y}_{R_1})^2+\sum_{i:x_i\in R_2}(y_i-\overline{y}_{R_2})^2,
 $$
 
-<p>
-which we want to minimize by considering all predictors
+<p>which we want to minimize by considering all predictors
 \( x_1,x_2,\dots,x_p \).  We consider also all possible values of \( s \) for
 each predictor. These values could be determined by randomly assigned
 numbers or by starting at the midpoint and then proceed till we find
 an optimal value.
+</p>
 
-<p>
-For any \( j \) and \( s \), we define the pair of half-planes where
+<p>For any \( j \) and \( s \), we define the pair of half-planes where
 \( \overline{y}_{R_1} \) is the mean response for the training
 observations in \( R_1(j,s) \), and \( \overline{y}_{R_2} \) is the mean
 response for the training observations in \( R_2(j,s) \).
+</p>
 
-<p>
-Finding the values of \( j \) and \( s \) that minimize the above equation can be
+<p>Finding the values of \( j \) and \( s \) that minimize the above equation can be
 done quite quickly, especially when the number of features \( p \) is not
 too large.
+</p>
 
-<p>
-Next, we repeat the process, looking
+<p>Next, we repeat the process, looking
 for the best predictor and best cutpoint in order to split the data
 further so as to minimize the MSE within each of the resulting
 regions. However, this time, instead of splitting the entire predictor
@@ -500,95 +549,83 @@ have three regions. Again, we look to split one of these three regions
 further, so as to minimize the MSE. The process continues until a
 stopping criterion is reached; for instance, we may continue until no
 region contains more than five observations.
+</p>
 
-<p>
 <!-- !split  -->
+<h2 id="pruning-the-tree">Pruning the tree </h2>
 
-<h2 id="___sec13">Pruning the tree </h2>
-
-<p>
-The above procedure is rather straightforward, but leads often to
+<p>The above procedure is rather straightforward, but leads often to
 overfitting and unnecessarily large and complicated trees. The basic
 idea is to grow a large tree \( T_0 \) and then prune it back in order to
 obtain a subtree. A smaller tree with fewer splits (fewer regions) can
 lead to smaller variance and better interpretation at the cost of a
 little more bias.
+</p>
 
-<p>
-The so-called Cost complexity pruning algorithm gives us a
+<p>The so-called Cost complexity pruning algorithm gives us a
 way to do just this. Rather than considering every possible subtree,
 we consider a sequence of trees indexed by a nonnegative tuning
 parameter \( \alpha \).
+</p>
 
-<p>
-Read more at the following <a href="https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py" target="_blank">Scikit-Learn link on pruning</a>.
+<p>Read more at the following <a href="https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py" target="_blank">Scikit-Learn link on pruning</a>.</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="cost-complexity-pruning">Cost complexity pruning </h2>
 
-<h2 id="___sec14">Cost complexity pruning </h2>
-
-<p>
-For each value of \( \alpha \)  there corresponds a subtree \( T \in T_0 \) such that
+<p>For each value of \( \alpha \)  there corresponds a subtree \( T \in T_0 \) such that</p>
 $$
 \sum_{m=1}^{\overline{T}}\sum_{i:x_i\in R_m}(y_i-\overline{y}_{R_m})^2+\alpha\overline{T},
 $$
 
-is as small as possible. Here \( \overline{T} \) is 
+<p>is as small as possible. Here \( \overline{T} \) is 
 the number of terminal nodes of the tree \( T \) , \( R_m \) is the
 rectangle (i.e. the subset of predictor space)  corresponding to the \( m \)-th terminal node.
+</p>
 
-<p>
-The tuning parameter \( \alpha \) controls a trade-off between the subtree&#8217;s
+<p>The tuning parameter \( \alpha \) controls a trade-off between the subtree&#8217;s
 complexity and its fit to the training data. When \( \alpha = 0 \), then the
 subtree \( T \) will simply equal \( T_0 \), 
 because then the above equation just measures the
 training error. 
 However, as \( \alpha \) increases, there is a price to pay for
 having a tree with many terminal nodes. The above equation will
-tend to be minimized for a smaller subtree.
+tend to be minimized for a smaller subtree. 
+</p>
 
-<p>
-It turns out that as we increase \( \alpha \) from zero
+<p>It turns out that as we increase \( \alpha \) from zero
 branches get pruned from the tree in a nested and predictable fashion,
 so obtaining the whole sequence of subtrees as a function of \( \alpha \) is
 easy. We can select a value of \( \alpha \) using a validation set or using
 cross-validation. We then return to the full data set and obtain the
-subtree corresponding to \( \alpha \).
+subtree corresponding to \( \alpha \). 
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="schematic-regression-procedure">Schematic Regression Procedure </h2>
 
-<h2 id="___sec15">Schematic Regression Procedure </h2>
-
-<p>
 <div class="alert alert-block alert-block alert-text-normal">
-<b>Building a Regression Tree.</b>
+<b>Building a Regression Tree</b>
 <p>
 
 <ol>
 <li> Use recursive binary splitting to grow a large tree on the training data, stopping only when each terminal node has fewer than some minimum number of observations.</li>
 <li> Apply cost complexity pruning to the large tree in order to obtain a sequence of best subtrees, as a function of \( \alpha \).</li>
 <li> Use for example \( K \)-fold cross-validation to choose \( \alpha \). Divide the training observations into \( K \) folds. For each \( k=1,2,\dots,K \) we:</li> 
-
 <ul>
   <li> repeat steps 1 and 2 on all but the \( k \)-th fold of the training data.</li> 
   <li> Then we valuate the mean squared prediction error on the data in the left-out \( k \)-th fold, as a function of \( \alpha \).</li>
   <li> Finally  we average the results for each value of \( \alpha \), and pick \( \alpha \) to minimize the average error.</li>
 </ul>
-
 <li> Return the subtree from Step 2 that corresponds to the chosen value of \( \alpha \).</li> 
 </ol>
 </div>
 
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="a-classification-tree">A Classification Tree </h2>
 
-<h2 id="___sec16">A Classification Tree </h2>
-
-<p>
-A classification tree is very similar to a regression tree, except
+<p>A classification tree is very similar to a regression tree, except
 that it is used to predict a qualitative response rather than a
 quantitative one. Recall that for a regression tree, the predicted
 response for an observation is given by the mean response of the
@@ -599,15 +636,13 @@ in the region to which it belongs. In interpreting the results of a
 classification tree, we are often interested not only in the class
 prediction corresponding to a particular terminal node region, but
 also in the class proportions among the training observations that
-fall into that region.
+fall into that region.  
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="growing-a-classification-tree">Growing a classification tree </h2>
 
-<h2 id="___sec17">Growing a classification tree </h2>
-
-<p>
-The task of growing a
+<p>The task of growing a
 classification tree is quite similar to the task of growing a
 regression tree. Just as in the regression setting, we use recursive
 binary splitting to grow a classification tree. However, in the
@@ -617,72 +652,69 @@ error rate</b>. Since we plan to assign an observation in a given region
 to the most commonly occurring error rate class of training
 observations in that region, the classification error rate is simply
 the fraction of the training observations in that region that do not
-belong to the most common class.
+belong to the most common class. 
+</p>
 
-<p>
-When building a classification tree, either the Gini index or the
+<p>When building a classification tree, either the Gini index or the
 entropy are typically used to evaluate the quality of a particular
 split, since these two approaches are more sensitive to node purity
-than is the classification error rate.
+than is the classification error rate. 
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="classification-tree-how-to-split-nodes">Classification tree, how to split nodes </h2>
 
-<h2 id="___sec18">Classification tree, how to split nodes </h2>
-
-<p>
-If our targets are the outcome of a classification process that takes
+<p>If our targets are the outcome of a classification process that takes
 for example \( k=1,2,\dots,K \) values, the only thing we need to think of
 is to set up the splitting criteria for each node.
+</p>
 
-<p>
-We define a PDF \( p_{mk} \) that represents the number of observations of
+<p>We define a PDF \( p_{mk} \) that represents the number of observations of
 a class \( k \) in a region \( R_m \) with \( N_m \) observations. We represent
 this likelihood function in terms of the proportion \( I(y_i=k) \) of
 observations of this class in the region \( R_m \) as
+</p>
 
 $$
 p_{mk} = \frac{1}{N_m}\sum_{x_i\in R_m}I(y_i=k).
 $$
 
-<p>
-We let \( p_{mk} \) represent the majority class of observations in region
+<p>We let \( p_{mk} \) represent the majority class of observations in region
 \( m \). The three most common ways of splitting a node are given by
+</p>
 
 <ul>
 <li> Misclassification error</li> 
 </ul>
-
 $$
 p_{mk} = \frac{1}{N_m}\sum_{x_i\in R_m}I(y_i\ne k) = 1-p_{mk}.
 $$
 
-
 <ul>
 <li> Gini index \( g \)</li>
 </ul>
-
 $$
 g = \sum_{k=1}^K p_{mk}(1-p_{mk}).
 $$
 
-
 <ul>
 <li> Information entropy or just entropy \( s \)</li>
 </ul>
-
 $$
 s = -\sum_{k=1}^K p_{mk}\log{p_{mk}}.
 $$
 
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec19">Visualizing the Tree, Classification </h2>
-<p>
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="visualizing-the-tree-classification">Visualizing the Tree, Classification </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">os</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">os</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_breast_cancer
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeClassifier
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
@@ -715,15 +747,32 @@ export_graphviz(
 )
 cmd = <span style="color: #CD5555">&#39;dot -Tpng DataFiles/cancer.dot -o DataFiles/cancer.png&#39;</span>
 os.system(cmd)
-</pre></div>
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 
-<h2 id="___sec20">Visualizing the Tree, The Moons  </h2>
-<p>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="visualizing-the-tree-the-moons">Visualizing the Tree, The Moons  </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #228B22"># Common imports</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #228B22"># Common imports</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span>  train_test_split 
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeClassifier
@@ -747,39 +796,72 @@ export_graphviz(
 )
 cmd = <span style="color: #CD5555">&#39;dot -Tpng DataFiles/moons.dot -o DataFiles/moons.png&#39;</span>
 os.system(cmd)
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="other-ways-of-visualizing-the-trees">Other ways of visualizing the trees </h2>
 
-<h2 id="___sec21">Other ways of visualizing the trees </h2>
+<p><b>Scikit-Learn</b> has also another way to visualize the trees which is very useful, here with the Iris data.</p>
 
-<p>
-<b>Scikit-Learn</b> has also another way to visualize the trees which is very useful, here with the Iris data.
-
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_iris
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_iris
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn</span> <span style="color: #8B008B; font-weight: bold">import</span> tree
 X, y = load_iris(return_X_y=<span style="color: #8B008B; font-weight: bold">True</span>)
 tree_clf = tree.DecisionTreeClassifier()
 tree_clf = tree_clf.fit(X, y)
 <span style="color: #228B22"># and then plot the tree</span>
 tree.plot_tree(tree_clf) 
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="printing-out-as-text">Printing out as text </h2>
 
-<h2 id="___sec22">Printing out as text </h2>
-
-<p>
-Alternatively, the tree can also be exported in textual format with the function exporttext.
+<p>Alternatively, the tree can also be exported in textual format with the function exporttext.
 This method doesn&#8217;t require the installation of external libraries and is more compact:
+</p>
 
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_iris
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_iris
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeClassifier
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> export_text
 iris = load_iris()
@@ -787,87 +869,92 @@ decision_tree = DecisionTreeClassifier(random_state=<span style="color: #B452CD"
 decision_tree = decision_tree.fit(iris.data, iris.target)
 r = export_text(decision_tree, feature_names=iris[<span style="color: #CD5555">&#39;feature_names&#39;</span>])
 <span style="color: #658b00">print</span>(r)
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="algorithms-for-setting-up-decision-trees">Algorithms for Setting up Decision Trees </h2>
 
-<h2 id="___sec23">Algorithms for Setting up Decision Trees </h2>
-
-<p>
-Two algorithms stand out in the set up of decision trees:
-
+<p>Two algorithms stand out in the set up of decision trees:</p>
 <ol>
 <li> The CART (Classification And Regression Tree) algorithm for both classification and regression</li>
 <li> The ID3 algorithm based on the computation of the information gain for classification</li>
 </ol>
-
-We discuss both algorithms with applications here. The popular library
+<p>We discuss both algorithms with applications here. The popular library
 <b>Scikit-Learn</b> uses the CART algorithm. For classification problems
 you can use either the <b>gini</b> index or the <b>entropy</b> to split a tree
 in two branches.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="the-cart-algorithm-for-classification">The CART algorithm for Classification </h2>
 
-<h2 id="___sec24">The CART algorithm for Classification </h2>
-
-<p>
-For classification, the CART algorithm splits the data set in two subsets using a single feature \( k \) and a threshold \( t_k \).
+<p>For classification, the CART algorithm splits the data set in two subsets using a single feature \( k \) and a threshold \( t_k \).
 This could be for example a threshold set by a number below a certain circumference of a malign tumor.
+</p>
 
-<p>
-How do we find these two quantities?
+<p>How do we find these two quantities?
 We search for the pair \( (k,t_k) \) that produces the purest subset using for example the <b>gini</b> factor \( G \).
 The cost function it tries to minimize is then
+</p>
 $$
 C(k,t_k) = \frac{m_{\mathrm{left}}}{m}G_{\mathrm{left}}+ \frac{m_{\mathrm{right}}}{m}G_{\mathrm{right}},
 $$
 
-where \( G_{\mathrm{left/right}} \) measures the impurity of the left/right subset  and \( m_{\mathrm{left/right}} \)
+<p>where \( G_{\mathrm{left/right}} \) measures the impurity of the left/right subset  and \( m_{\mathrm{left/right}} \)
  is the number of instances in the left/right subset
+</p>
 
-<p>
-Once it has successfully split the training set in two, it splits the subsets using the same logic, then the subsubsets
+<p>Once it has successfully split the training set in two, it splits the subsets using the same logic, then the subsubsets
 and so on, recursively. It stops recursing once it reaches the maximum depth (defined by the
 \( max\_depth \) hyperparameter), or if it cannot find a split that will reduce impurity. A few other
 hyperparameters control additional stopping conditions such as the \( min\_samples\_split \),
 \( min\_samples\_leaf \), \( min\_weight\_fraction\_leaf \), and \( max\_leaf\_nodes \).
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="the-cart-algorithm-for-regression">The CART algorithm for Regression </h2>
 
-<h2 id="___sec25">The CART algorithm for Regression </h2>
-
-<p>
-The CART algorithm for regression works is similar to the one for classification except that instead of trying to split the
+<p>The CART algorithm for regression works is similar to the one for classification except that instead of trying to split the
 training set in a way that minimizes say the <b>gini</b> or <b>entropy</b> impurity, it now tries to split the training set in a way that minimizes our well-known mean-squared error (MSE). The cost function is now
+</p>
 $$
 C(k,t_k) = \frac{m_{\mathrm{left}}}{m}\mathrm{MSE}_{\mathrm{left}}+ \frac{m_{\mathrm{right}}}{m}\mathrm{MSE}_{\mathrm{right}}.
 $$
 
-Here the MSE for a specific node is defined as
+<p>Here the MSE for a specific node is defined as</p>
 $$
 \mathrm{MSE}_{\mathrm{node}}=\frac{1}{m_\mathrm{node}}\sum_{i\in \mathrm{node}}(\overline{y}_{\mathrm{node}}-y_i)^2,
 $$
 
-with
+<p>with</p>
 $$
 \overline{y}_{\mathrm{node}}=\frac{1}{m_\mathrm{node}}\sum_{i\in \mathrm{node}}y_i,
 $$
 
-the mean value of all observations in a specific node.
+<p>the mean value of all observations in a specific node.</p>
 
-<p>
-Without any regularization, the regression task for decision trees, 
+<p>Without any regularization, the regression task for decision trees, 
 just like for classification tasks, is  prone to overfitting.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="computing-the-gini-index">Computing the Gini index </h2>
 
-<h2 id="___sec26">Computing the Gini index </h2>
-
-<p>
-The example we will look at is a classical one in many Machine
+<p>The example we will look at is a classical one in many Machine
 Learning applications. Based on various meteorological features, we
 have several so-called attributes which decide whether we at the end
 will do some outdoor activity like skiing, going for a bike ride etc
@@ -877,10 +964,10 @@ etc.  The table here contains the feautures <b>outlook</b>, <b>temperature</b>,
 attributes for each feature are then sunny, overcast and rain for the
 outlook, hot, cold and mild for temperature, high and normal for
 humidity and weak and strong for wind.
+</p>
 
-<p>
-The table here summarizes the various attributes and
-<table border="1">
+<p>The table here summarizes the various attributes and</p>
+<table class="dotable" border="1">
 <thead>
 <tr><th align="center">Day</th> <th align="center">Outlook </th> <th align="center">Temperature</th> <th align="center">Humidity</th> <th align="center"> Wind </th> <th align="center">Ride</th> </tr>
 </thead>
@@ -901,15 +988,18 @@ The table here summarizes the various attributes and
 <tr><td align="center">   14     </td> <td align="center">   Rain        </td> <td align="center">   Mild           </td> <td align="center">   High        </td> <td align="center">   Strong    </td> <td align="center">   0       </td> </tr>
 </tbody>
 </table>
-<p>
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="simple-python-code-to-read-in-data-and-perform-classification">Simple Python Code to read in Data and perform Classification </h2>
 
-<h2 id="___sec27">Simple Python Code to read in Data and perform Classification </h2>
-
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #228B22"># Common imports</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #228B22"># Common imports</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">pandas</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">pd</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
@@ -976,25 +1066,41 @@ export_graphviz(
 )
 cmd = <span style="color: #CD5555">&#39;dot -Tpng DataFiles/cancer.dot -o DataFiles/cancer.png&#39;</span>
 os.system(cmd)
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="computing-the-gini-factor">Computing the Gini Factor  </h2>
 
-<h2 id="___sec28">Computing the Gini Factor  </h2>
-
-<p>
-The above functions (gini, entropy and misclassification error) are
+<p>The above functions (gini, entropy and misclassification error) are
 important components of the so-called CART algorithm. We will discuss
 this algorithm below after we have discussed the information gain
 algorithm ID3.
+</p>
 
-<p>
-In the example here we have converted all our attributes into numerical values \( 0,1,2 \) etc.
+<p>In the example here we have converted all our attributes into numerical values \( 0,1,2 \) etc.</p>
 
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #228B22"># Split a dataset based on an attribute and an attribute value</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #228B22"># Split a dataset based on an attribute and an attribute value</span>
 <span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">test_split</span>(index, value, dataset):
 	left, right = <span style="color: #658b00">list</span>(), <span style="color: #658b00">list</span>()
 	<span style="color: #8B008B; font-weight: bold">for</span> row <span style="color: #8B008B">in</span> dataset:
@@ -1054,15 +1160,28 @@ dataset = [[<span style="color: #B452CD">0</span>,<span style="color: #B452CD">0
 
 split = get_split(dataset)
 <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Split: [X%d &lt; %.3f]&#39;</span> % ((split[<span style="color: #CD5555">&#39;index&#39;</span>]+<span style="color: #B452CD">1</span>), split[<span style="color: #CD5555">&#39;value&#39;</span>]))
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="entropy-and-the-id3-algorithm">Entropy and the ID3 algorithm </h2>
 
-<h2 id="___sec29">Entropy and the ID3 algorithm </h2>
-
-<p>
-The ID3 algorithm  learns decision trees by constructing
+<p>The ID3 algorithm  learns decision trees by constructing
 them in a top down way, beginning with the question <b>which attribute should be tested at the root of the tree</b>?
+</p>
 
 <ol>
 <li> Each instance attribute is evaluated using a statistical test to determine how well it alone classifies the training examples.</li>
@@ -1072,33 +1191,34 @@ them in a top down way, beginning with the question <b>which attribute should be
 <li> The entire process is then repeated using the training examples associated with each descendant node to select the best attribute to test at that point in the tree.</li>
 <li> This forms a greedy search for an acceptable decision tree, in which the algorithm never backtracks to reconsider earlier choices.</li> 
 </ol>
-
-The ID3 algorithm selects which attribute to test at each node in the
+<p>The ID3 algorithm selects which attribute to test at each node in the
 tree.
+</p>
 
-<p>
-We would like to select the attribute that is most useful for classifying
+<p>We would like to select the attribute that is most useful for classifying
 examples.
+</p>
 
-<p>
-What is a good quantitative measure of the worth of an attribute?
+<p>What is a good quantitative measure of the worth of an attribute?</p>
 
-<p>
-Information gain measures how well a given attribute separates the
+<p>Information gain measures how well a given attribute separates the
 training examples according to their target classification.
+</p>
 
-<p>
-The ID3 algorithm uses this information gain measure to select among the candidate
+<p>The ID3 algorithm uses this information gain measure to select among the candidate
 attributes at each step while growing the tree.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
-<h2 id="___sec30">Cancer Data again now with Decision Trees and other Methods </h2>
-<p>
+<h2 id="cancer-data-again-now-with-decision-trees-and-other-methods">Cancer Data again now with Decision Trees and other Methods </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span>  train_test_split 
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_breast_cancer
@@ -1139,15 +1259,32 @@ svm.fit(X_train_scaled, y_train)
 <span style="color: #228B22"># Decision Trees</span>
 deep_tree_clf.fit(X_train_scaled, y_train)
 <span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Test set accuracy with Decision Trees and scaled data: {:.2f}&quot;</span>.format(deep_tree_clf.score(X_test_scaled,y_test)))
-</pre></div>
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 
-<h2 id="___sec31">Another example, the moons again </h2>
-<p>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="another-example-the-moons-again">Another example, the moons again </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">__future__</span> <span style="color: #8B008B; font-weight: bold">import</span> division, print_function, unicode_literals
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">__future__</span> <span style="color: #8B008B; font-weight: bold">import</span> division, print_function, unicode_literals
 
 <span style="color: #228B22"># Common imports</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
@@ -1211,15 +1348,32 @@ plt.subplot(<span style="color: #B452CD">122</span>)
 plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-<span style="color: #B452CD">1.5</span>, <span style="color: #B452CD">2.5</span>, -<span style="color: #B452CD">1</span>, <span style="color: #B452CD">1.5</span>], iris=<span style="color: #8B008B; font-weight: bold">False</span>)
 plt.title(<span style="color: #CD5555">&quot;min_samples_leaf = {}&quot;</span>.format(deep_tree_clf2.min_samples_leaf), fontsize=<span style="color: #B452CD">14</span>)
 plt.show()
-</pre></div>
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 
-<h2 id="___sec32">Playing around with regions </h2>
-<p>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="playing-around-with-regions">Playing around with regions </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span>np.random.seed(<span style="color: #B452CD">6</span>)
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;">np.random.seed(<span style="color: #B452CD">6</span>)
 Xs = np.random.rand(<span style="color: #B452CD">100</span>, <span style="color: #B452CD">2</span>) - <span style="color: #B452CD">0.5</span>
 ys = (Xs[:, <span style="color: #B452CD">0</span>] &gt; <span style="color: #B452CD">0</span>).astype(np.float32) * <span style="color: #B452CD">2</span>
 
@@ -1239,37 +1393,86 @@ plt.subplot(<span style="color: #B452CD">122</span>)
 plot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-<span style="color: #B452CD">0.7</span>, <span style="color: #B452CD">0.7</span>, -<span style="color: #B452CD">0.7</span>, <span style="color: #B452CD">0.7</span>], iris=<span style="color: #8B008B; font-weight: bold">False</span>)
 
 plt.show()
-</pre></div>
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 
-<h2 id="___sec33">Regression trees </h2>
-<p>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="regression-trees">Regression trees </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #228B22"># Quadratic training set + noise</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #228B22"># Quadratic training set + noise</span>
 np.random.seed(<span style="color: #B452CD">42</span>)
 m = <span style="color: #B452CD">200</span>
 X = np.random.rand(m, <span style="color: #B452CD">1</span>)
 y = <span style="color: #B452CD">4</span> * (X - <span style="color: #B452CD">0.5</span>) ** <span style="color: #B452CD">2</span>
 y = y + np.random.randn(m, <span style="color: #B452CD">1</span>) / <span style="color: #B452CD">10</span>
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeRegressor
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeRegressor
 
 tree_reg = DecisionTreeRegressor(max_depth=<span style="color: #B452CD">2</span>, random_state=<span style="color: #B452CD">42</span>)
 tree_reg.fit(X, y)
-</pre></div>
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 
-<h2 id="___sec34">Final regressor code </h2>
-<p>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="final-regressor-code">Final regressor code </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeRegressor
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeRegressor
 
 tree_reg1 = DecisionTreeRegressor(random_state=<span style="color: #B452CD">42</span>, max_depth=<span style="color: #B452CD">2</span>)
 tree_reg2 = DecisionTreeRegressor(random_state=<span style="color: #B452CD">42</span>, max_depth=<span style="color: #B452CD">3</span>)
@@ -1307,11 +1510,26 @@ plt.text(<span style="color: #B452CD">0.3</span>, <span style="color: #B452CD">0
 plt.title(<span style="color: #CD5555">&quot;max_depth=3&quot;</span>, fontsize=<span style="color: #B452CD">14</span>)
 
 plt.show()
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span>tree_reg1 = DecisionTreeRegressor(random_state=<span style="color: #B452CD">42</span>)
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;">tree_reg1 = DecisionTreeRegressor(random_state=<span style="color: #B452CD">42</span>)
 tree_reg2 = DecisionTreeRegressor(random_state=<span style="color: #B452CD">42</span>, min_samples_leaf=<span style="color: #B452CD">10</span>)
 tree_reg1.fit(X, y)
 tree_reg2.fit(X, y)
@@ -1339,11 +1557,24 @@ plt.xlabel(<span style="color: #CD5555">&quot;$x_1$&quot;</span>, fontsize=<span
 plt.title(<span style="color: #CD5555">&quot;min_samples_leaf={}&quot;</span>.format(tree_reg2.min_samples_leaf), fontsize=<span style="color: #B452CD">14</span>)
 
 plt.show()
-</pre></div>
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 
-<h2 id="___sec35">Pros and cons of trees, pros </h2>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="pros-and-cons-of-trees-pros">Pros and cons of trees, pros </h2>
 
 <ul>
 <li> White box, easy to interpret model. Some people believe that decision trees more closely mirror human decision-making than do the regression and classification approaches discussed earlier (think of support vector machines)</li>
@@ -1354,10 +1585,8 @@ plt.show()
 <li> Can model interactions between the different descriptive features</li>
 <li> Trees can be displayed graphically, and are easily interpreted even by a non-expert (especially if they are small)</li>
 </ul>
-
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
-<h2 id="___sec36">Disadvantages </h2>
+<h2 id="disadvantages">Disadvantages </h2>
 
 <ul>
 <li> Unfortunately, trees generally do not have the same level of predictive accuracy as some of the other regression and classification approaches</li>
@@ -1368,28 +1597,26 @@ plt.show()
 <li> If the number of features is relatively large (high dimensional) and the number of instances is relatively low, the tree might overfit the data</li>
 <li> Features with many levels may be preferred over features with less levels since for them it is <em>more easy</em> to split the dataset such that the sub datasets only contain pure target feature values. This issue can be addressed by preferring for instance the information gain ratio as splitting criteria over information gain</li>
 </ul>
-
-However, by aggregating many decision trees, using methods like
+<p>However, by aggregating many decision trees, using methods like
 bagging, random forests, and boosting, the predictive performance of
 trees can be substantially improved.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="ensemble-methods-from-a-single-tree-to-many-trees-and-extreme-boosting-meet-the-jungle-of-methods">Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods </h2>
 
-<h2 id="___sec37">Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods </h2>
-
-<p>
-As stated above and seen in many of the examples discussed here about
+<p>As stated above and seen in many of the examples discussed here about
 a single decision tree, we often end up overfitting our training
 data. This normally means that we have a high variance. Can we reduce
 the variance of a statistical learning method?
+</p>
 
-<p>
-This leads us to a set of different methods that can combine different
+<p>This leads us to a set of different methods that can combine different
 machine learning algorithms or just use one of them to construct
 forests and jungles of trees, homogeneous ones or heterogenous
 ones. These methods are recognized by different names which we will
 try to explain here. These are
+</p>
 
 <ol>
 <li> Voting classifiers</li>
@@ -1397,50 +1624,45 @@ try to explain here. These are
 <li> Random forests</li>
 <li> Boosting methods, from adaptive to Extreme Gradient Boosting (XGBoost)</li>
 </ol>
+<p>We discuss these methods here.</p>
 
-We discuss these methods here.
-
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="an-overview-of-ensemble-methods">An Overview of Ensemble Methods  </h2>
 
-<h2 id="___sec38">An Overview of Ensemble Methods  </h2>
+<br/><br/>
+<center>
+<p><img src="DataFiles/ensembleoverview.png" width="600" align="bottom"></p>
+</center>
+<br/><br/>
 
-<p>
-<br /><br /><center><p><img src="DataFiles/ensembleoverview.png" align="bottom" width=600></p></center><br /><br />
-
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="bagging">Bagging </h2>
 
-<h2 id="___sec39">Bagging </h2>
-
-<p>
-The <b>plain</b> decision trees suffer from high
+<p>The <b>plain</b> decision trees suffer from high
 variance. This means that if we split the training data into two parts
 at random, and fit a decision tree to both halves, the results that we
 get could be quite different. In contrast, a procedure with low
 variance will yield similar results if applied repeatedly to distinct
 data sets; linear regression tends to have low variance, if the ratio
-of \( n \) to \( p \) is moderately large.
+of \( n \) to \( p \) is moderately large. 
+</p>
 
-<p>
-<b>Bootstrap aggregation</b>, or just <b>bagging</b>, is a
+<p><b>Bootstrap aggregation</b>, or just <b>bagging</b>, is a
 general-purpose procedure for reducing the variance of a statistical
-learning method.
+learning method. 
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="more-bagging">More bagging </h2>
 
-<h2 id="___sec40">More bagging </h2>
-
-<p>
-Bagging typically results in improved accuracy
+<p>Bagging typically results in improved accuracy
 over prediction using a single tree. Unfortunately, however, it can be
 difficult to interpret the resulting model. Recall that one of the
 advantages of decision trees is the attractive and easily interpreted
 diagram that results.
+</p>
 
-<p>
-However, when we bag a large number of trees, it is no longer
+<p>However, when we bag a large number of trees, it is no longer
 possible to represent the resulting statistical learning procedure
 using a single tree, and it is no longer clear which variables are
 most important to the procedure. Thus, bagging improves prediction
@@ -1455,15 +1677,18 @@ trees. A large value indicates an important predictor. Similarly, in
 the context of bagging classification trees, we can add up the total
 amount that the Gini index  is decreased by splits over a given
 predictor, averaged over all \( B \) trees.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
-<h2 id="___sec41">Simple Voting Example, head or tail </h2>
-<p>
+<h2 id="simple-voting-example-head-or-tail">Simple Voting Example, head or tail </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span>heads_proba = <span style="color: #B452CD">0.51</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;">heads_proba = <span style="color: #B452CD">0.51</span>
 coin_tosses = (np.random.rand(<span style="color: #B452CD">10000</span>, <span style="color: #B452CD">10</span>) &lt; heads_proba).astype(np.int32)
 cumulative_heads_ratio = np.cumsum(coin_tosses, axis=<span style="color: #B452CD">0</span>) / np.arange(<span style="color: #B452CD">1</span>, <span style="color: #B452CD">10001</span>).reshape(-<span style="color: #B452CD">1</span>, <span style="color: #B452CD">1</span>)
 plt.figure(figsize=(<span style="color: #B452CD">8</span>,<span style="color: #B452CD">3.5</span>))
@@ -1476,15 +1701,32 @@ plt.legend(loc=<span style="color: #CD5555">&quot;lower right&quot;</span>)
 plt.axis([<span style="color: #B452CD">0</span>, <span style="color: #B452CD">10000</span>, <span style="color: #B452CD">0.42</span>, <span style="color: #B452CD">0.58</span>])
 save_fig(<span style="color: #CD5555">&quot;votingsimple&quot;</span>)
 plt.show()
-</pre></div>
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 
-<h2 id="___sec42">Using the Voting Classifier </h2>
-<p>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="using-the-voting-classifier">Using the Voting Classifier </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> make_moons
 
 X, y = make_moons(n_samples=<span style="color: #B452CD">500</span>, noise=<span style="color: #B452CD">0.30</span>, random_state=<span style="color: #B452CD">42</span>)
@@ -1527,16 +1769,33 @@ voting_clf.fit(X_train, y_train)
     clf.fit(X_train, y_train)
     y_pred = clf.predict(X_test)
     <span style="color: #658b00">print</span>(clf.<span style="color: #00688B">__class__</span>.<span style="color: #00688B">__name__</span>, accuracy_score(y_test, y_pred))
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="please-not-the-moons-again-voting-and-bagging">Please, not the moons again! Voting and Bagging </h2>
 
-<h2 id="___sec43">Please, not the moons again! Voting and Bagging </h2>
-
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> make_moons
 
 X, y = make_moons(n_samples=<span style="color: #B452CD">500</span>, noise=<span style="color: #B452CD">0.30</span>, random_state=<span style="color: #B452CD">42</span>)
@@ -1554,21 +1813,51 @@ voting_clf = VotingClassifier(
     estimators=[(<span style="color: #CD5555">&#39;lr&#39;</span>, log_clf), (<span style="color: #CD5555">&#39;rf&#39;</span>, rnd_clf), (<span style="color: #CD5555">&#39;svc&#39;</span>, svm_clf)],
     voting=<span style="color: #CD5555">&#39;hard&#39;</span>)
 voting_clf.fit(X_train, y_train)
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> accuracy_score
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> accuracy_score
 
 <span style="color: #8B008B; font-weight: bold">for</span> clf <span style="color: #8B008B">in</span> (log_clf, rnd_clf, svm_clf, voting_clf):
     clf.fit(X_train, y_train)
     y_pred = clf.predict(X_test)
     <span style="color: #658b00">print</span>(clf.<span style="color: #00688B">__class__</span>.<span style="color: #00688B">__name__</span>, accuracy_score(y_test, y_pred))
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span>log_clf = LogisticRegression(random_state=<span style="color: #B452CD">42</span>)
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;">log_clf = LogisticRegression(random_state=<span style="color: #B452CD">42</span>)
 rnd_clf = RandomForestClassifier(random_state=<span style="color: #B452CD">42</span>)
 svm_clf = SVC(probability=<span style="color: #8B008B; font-weight: bold">True</span>, random_state=<span style="color: #B452CD">42</span>)
 
@@ -1576,26 +1865,58 @@ voting_clf = VotingClassifier(
     estimators=[(<span style="color: #CD5555">&#39;lr&#39;</span>, log_clf), (<span style="color: #CD5555">&#39;rf&#39;</span>, rnd_clf), (<span style="color: #CD5555">&#39;svc&#39;</span>, svm_clf)],
     voting=<span style="color: #CD5555">&#39;soft&#39;</span>)
 voting_clf.fit(X_train, y_train)
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> accuracy_score
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> accuracy_score
 
 <span style="color: #8B008B; font-weight: bold">for</span> clf <span style="color: #8B008B">in</span> (log_clf, rnd_clf, svm_clf, voting_clf):
     clf.fit(X_train, y_train)
     y_pred = clf.predict(X_test)
     <span style="color: #658b00">print</span>(clf.<span style="color: #00688B">__class__</span>.<span style="color: #00688B">__name__</span>, accuracy_score(y_test, y_pred))
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="bagging-examples">Bagging Examples  </h2>
 
-<h2 id="___sec44">Bagging Examples  </h2>
-
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.ensemble</span> <span style="color: #8B008B; font-weight: bold">import</span> BaggingClassifier
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.ensemble</span> <span style="color: #8B008B; font-weight: bold">import</span> BaggingClassifier
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeClassifier
 
 bag_clf = BaggingClassifier(
@@ -1603,25 +1924,70 @@ bag_clf = BaggingClassifier(
     max_samples=<span style="color: #B452CD">100</span>, bootstrap=<span style="color: #8B008B; font-weight: bold">True</span>, n_jobs=-<span style="color: #B452CD">1</span>, random_state=<span style="color: #B452CD">42</span>)
 bag_clf.fit(X_train, y_train)
 y_pred = bag_clf.predict(X_test)
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> accuracy_score
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> accuracy_score
 <span style="color: #658b00">print</span>(accuracy_score(y_test, y_pred))
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span>tree_clf = DecisionTreeClassifier(random_state=<span style="color: #B452CD">42</span>)
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;">tree_clf = DecisionTreeClassifier(random_state=<span style="color: #B452CD">42</span>)
 tree_clf.fit(X_train, y_train)
 y_pred_tree = tree_clf.predict(X_test)
 <span style="color: #658b00">print</span>(accuracy_score(y_test, y_pred_tree))
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">matplotlib.colors</span> <span style="color: #8B008B; font-weight: bold">import</span> ListedColormap
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">matplotlib.colors</span> <span style="color: #8B008B; font-weight: bold">import</span> ListedColormap
 
 <span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">plot_decision_boundary</span>(clf, X, y, axes=[-<span style="color: #B452CD">1.5</span>, <span style="color: #B452CD">2.5</span>, -<span style="color: #B452CD">1</span>, <span style="color: #B452CD">1.5</span>], alpha=<span style="color: #B452CD">0.5</span>, contour=<span style="color: #8B008B; font-weight: bold">True</span>):
     x1s = np.linspace(axes[<span style="color: #B452CD">0</span>], axes[<span style="color: #B452CD">1</span>], <span style="color: #B452CD">100</span>)
@@ -1648,19 +2014,36 @@ plot_decision_boundary(bag_clf, X, y)
 plt.title(<span style="color: #CD5555">&quot;Decision Trees with Bagging&quot;</span>, fontsize=<span style="color: #B452CD">14</span>)
 save_fig(<span style="color: #CD5555">&quot;baggingtree&quot;</span>)
 plt.show()
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="making-your-own-bootstrap-changing-the-level-of-the-decision-tree">Making your own Bootstrap: Changing the Level of the Decision Tree </h2>
 
-<h2 id="___sec45">Making your own Bootstrap: Changing the Level of the Decision Tree </h2>
-
-<p>
-Let us bring up our good old boostrap example from the linear regression lectures. We change the linerar regression algorithm with
+<p>Let us bring up our good old boostrap example from the linear regression lectures. We change the linerar regression algorithm with
 a decision tree wth different depths and perform a bootstrap aggregate (in this case we perform as many bootstraps as data points \( n \)).
-<p>
+</p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
-<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #eeeedd">
+  <pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.pipeline</span> <span style="color: #8B008B; font-weight: bold">import</span> make_pipeline
@@ -1717,18 +2100,26 @@ plt.plot(polydegree, variance, label=<span style="color: #CD5555">&#39;Variance&
 plt.legend()
 save_fig(<span style="color: #CD5555">&quot;baggingboot&quot;</span>)
 plt.show()
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
 
 <!-- ------------------- end of main content --------------- -->
-
-
 <center style="font-size:80%">
-<!-- copyright --> &copy; 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
+<!-- copyright --> &copy; 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
 </center>
-
-
 </body>
 </html>
-    
 
diff --git a/doc/pub/week44/html/week44.html b/doc/pub/week44/html/week44.html
index 85eff62cf..fe6aa4c39 100644
--- a/doc/pub/week44/html/week44.html
+++ b/doc/pub/week44/html/week44.html
@@ -1,36 +1,105 @@
 <!--
-Automatically generated HTML file from DocOnce source
-(https://github.com/hplgit/doconce/)
+HTML file automatically generated from DocOnce source
+(https://github.com/doconce/doconce/)
+doconce format html week44.do.txt --pygments_html_style=default --html_style=bloodish --html_links_in_new_window --html_output=week44 --no_mako
 -->
 <html>
 <head>
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-<meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
+<meta name="generator" content="DocOnce: https://github.com/doconce/doconce/" />
 <meta name="viewport" content="width=device-width, initial-scale=1.0" />
 <meta name="description" content="Week 44: From Decision Trees to Bagging methods">
-
 <title>Week 44: From Decision Trees to Bagging methods</title>
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 p.caption { width: 80%; font-style: normal; text-align: left; }
-hr.figure { border: 0; width: 80%; border-bottom: 1px solid #aaa}
+hr.figure { border: 0; width: 80%; border-bottom: 1px solid #aaa; }div.highlight {
+    border: 1px solid #cfcfcf;
+    border-radius: 2px;
+    line-height: 1.21429em;
+}
+div.cell {
+    width: 100%;
+    padding: 5px 5px 5px 0;
+    margin: 0;
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+}
+div.input {
+    page-break-inside: avoid;
+    box-orient: horizontal;
+    box-align: stretch;
+    display: flex;
+    flex-direction: row;
+    align-items: stretch;
+}
+div.inner_cell {
+    box-orient: vertical;
+    box-align: stretch;
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+    box-flex: 1;
+    flex: 1;
+}
+div.input_area {
+    border: 1px solid #cfcfcf;
+    border-radius: 4px;
+    background: #f7f7f7;
+    line-height: 1.21429em;
+}
+div.input_area > div.highlight {
+    margin: .4em;
+    border: none;
+    padding: 0;
+    background-color: transparent;
+}
+div.output_wrapper {
+    position: relative;
+    box-orient: vertical;
+    box-align: stretch;
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+}
+.output {
+    box-orient: vertical;
+    box-align: stretch;
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+}
+div.output_area {
+    padding: 0;
+    page-break-inside: avoid;
+    box-orient: horizontal;
+    box-align: stretch;
+    display: flex;
+    flex-direction: row;
+    align-items: stretch;
+}
+div.output_subarea {
+    padding: .4em .4em 0 .4em;
+    box-flex: 1;
+    flex: 1;
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+div.output_text {
+    text-align: left;
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+}
 .alert-text-small   { font-size: 80%;  }
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+.alert-warning { background-image: url(https://cdn.rawgit.com/doconce/doconce/master/bundled/html_images/small_gray_warning.png); }
+.alert-question {background-image:url(https://cdn.rawgit.com/doconce/doconce/master/bundled/html_images/small_gray_question.png); }
 div { text-align: justify; text-justify: inter-word; }
+.tab {
+  padding-left: 1.5em;
+}
+div.toc p,a {
+  line-height: 1.3;
+  margin-top: 1.1;
+  margin-bottom: 1.1;
+}
 </style>
-
-
 </head>
 
 <!-- tocinfo
 {'highest level': 2,
- 'sections': [('Overview of week 44', 2, None, '___sec0'),
-              ('Thursday', 2, None, '___sec1'),
-              ('Decision trees, overarching aims', 2, None, '___sec2'),
-              ('Basics of a tree', 2, None, '___sec3'),
-              ('A Sketch of a Tree, Regression problem', 2, None, '___sec4'),
+ 'sections': [('Overview of week 44', 2, None, 'overview-of-week-44'),
+              ('Thursday', 2, None, 'thursday'),
+              ('Decision trees, overarching aims',
+               2,
+               None,
+               'decision-trees-overarching-aims'),
+              ('Basics of a tree', 2, None, 'basics-of-a-tree'),
+              ('A Sketch of a Tree, Regression problem',
+               2,
+               None,
+               'a-sketch-of-a-tree-regression-problem'),
               ('A Sketch of a Tree, Classification  problem',
                2,
                None,
-               '___sec5'),
+               'a-sketch-of-a-tree-classification-problem'),
               ('A typical Decision Tree with its pertinent Jargon, '
                'Classification Problem',
                2,
                None,
-               '___sec6'),
-              ('General Features', 2, None, '___sec7'),
-              ('How do we set it up?', 2, None, '___sec8'),
-              ('Decision trees and Regression', 2, None, '___sec9'),
-              ('Building a tree, regression', 2, None, '___sec10'),
+               'a-typical-decision-tree-with-its-pertinent-jargon-classification-problem'),
+              ('General Features', 2, None, 'general-features'),
+              ('How do we set it up?', 2, None, 'how-do-we-set-it-up'),
+              ('Decision trees and Regression',
+               2,
+               None,
+               'decision-trees-and-regression'),
+              ('Building a tree, regression',
+               2,
+               None,
+               'building-a-tree-regression'),
               ('A top-down approach, recursive binary splitting',
                2,
                None,
-               '___sec11'),
-              ('Making a tree', 2, None, '___sec12'),
-              ('Pruning the tree', 2, None, '___sec13'),
-              ('Cost complexity pruning', 2, None, '___sec14'),
-              ('Schematic Regression Procedure', 2, None, '___sec15'),
-              ('A Classification Tree', 2, None, '___sec16'),
-              ('Growing a classification tree', 2, None, '___sec17'),
-              ('Classification tree, how to split nodes', 2, None, '___sec18'),
-              ('Visualizing the Tree, Classification', 2, None, '___sec19'),
-              ('Visualizing the Tree, The Moons', 2, None, '___sec20'),
-              ('Other ways of visualizing the trees', 2, None, '___sec21'),
-              ('Printing out as text', 2, None, '___sec22'),
-              ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
-              ('The CART algorithm for Classification', 2, None, '___sec24'),
-              ('The CART algorithm for Regression', 2, None, '___sec25'),
-              ('Computing the Gini index', 2, None, '___sec26'),
+               'a-top-down-approach-recursive-binary-splitting'),
+              ('Making a tree', 2, None, 'making-a-tree'),
+              ('Pruning the tree', 2, None, 'pruning-the-tree'),
+              ('Cost complexity pruning', 2, None, 'cost-complexity-pruning'),
+              ('Schematic Regression Procedure',
+               2,
+               None,
+               'schematic-regression-procedure'),
+              ('A Classification Tree', 2, None, 'a-classification-tree'),
+              ('Growing a classification tree',
+               2,
+               None,
+               'growing-a-classification-tree'),
+              ('Classification tree, how to split nodes',
+               2,
+               None,
+               'classification-tree-how-to-split-nodes'),
+              ('Visualizing the Tree, Classification',
+               2,
+               None,
+               'visualizing-the-tree-classification'),
+              ('Visualizing the Tree, The Moons',
+               2,
+               None,
+               'visualizing-the-tree-the-moons'),
+              ('Other ways of visualizing the trees',
+               2,
+               None,
+               'other-ways-of-visualizing-the-trees'),
+              ('Printing out as text', 2, None, 'printing-out-as-text'),
+              ('Algorithms for Setting up Decision Trees',
+               2,
+               None,
+               'algorithms-for-setting-up-decision-trees'),
+              ('The CART algorithm for Classification',
+               2,
+               None,
+               'the-cart-algorithm-for-classification'),
+              ('The CART algorithm for Regression',
+               2,
+               None,
+               'the-cart-algorithm-for-regression'),
+              ('Computing the Gini index', 2, None, 'computing-the-gini-index'),
               ('Simple Python Code to read in Data and perform Classification',
                2,
                None,
-               '___sec27'),
-              ('Computing the Gini Factor', 2, None, '___sec28'),
-              ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
+               'simple-python-code-to-read-in-data-and-perform-classification'),
+              ('Computing the Gini Factor',
+               2,
+               None,
+               'computing-the-gini-factor'),
+              ('Entropy and the ID3 algorithm',
+               2,
+               None,
+               'entropy-and-the-id3-algorithm'),
               ('Cancer Data again now with Decision Trees and other Methods',
                2,
                None,
-               '___sec30'),
-              ('Another example, the moons again', 2, None, '___sec31'),
-              ('Playing around with regions', 2, None, '___sec32'),
-              ('Regression trees', 2, None, '___sec33'),
-              ('Final regressor code', 2, None, '___sec34'),
-              ('Pros and cons of trees, pros', 2, None, '___sec35'),
-              ('Disadvantages', 2, None, '___sec36'),
+               'cancer-data-again-now-with-decision-trees-and-other-methods'),
+              ('Another example, the moons again',
+               2,
+               None,
+               'another-example-the-moons-again'),
+              ('Playing around with regions',
+               2,
+               None,
+               'playing-around-with-regions'),
+              ('Regression trees', 2, None, 'regression-trees'),
+              ('Final regressor code', 2, None, 'final-regressor-code'),
+              ('Pros and cons of trees, pros',
+               2,
+               None,
+               'pros-and-cons-of-trees-pros'),
+              ('Disadvantages', 2, None, 'disadvantages'),
               ('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
                'Boosting, Meet the Jungle of Methods',
                2,
                None,
-               '___sec37'),
-              ('An Overview of Ensemble Methods', 2, None, '___sec38'),
-              ('Bagging', 2, None, '___sec39'),
-              ('More bagging', 2, None, '___sec40'),
-              ('Simple Voting Example, head or tail', 2, None, '___sec41'),
-              ('Using the Voting Classifier', 2, None, '___sec42'),
+               'ensemble-methods-from-a-single-tree-to-many-trees-and-extreme-boosting-meet-the-jungle-of-methods'),
+              ('An Overview of Ensemble Methods',
+               2,
+               None,
+               'an-overview-of-ensemble-methods'),
+              ('Bagging', 2, None, 'bagging'),
+              ('More bagging', 2, None, 'more-bagging'),
+              ('Simple Voting Example, head or tail',
+               2,
+               None,
+               'simple-voting-example-head-or-tail'),
+              ('Using the Voting Classifier',
+               2,
+               None,
+               'using-the-voting-classifier'),
               ('Please, not the moons again! Voting and Bagging',
                2,
                None,
-               '___sec43'),
-              ('Bagging Examples', 2, None, '___sec44'),
+               'please-not-the-moons-again-voting-and-bagging'),
+              ('Bagging Examples', 2, None, 'bagging-examples'),
               ('Making your own Bootstrap: Changing the Level of the Decision '
                'Tree',
                2,
                None,
-               '___sec45')]}
+               'making-your-own-bootstrap-changing-the-level-of-the-decision-tree')]}
 end of tocinfo -->
 
 <body>
@@ -158,131 +295,110 @@ MathJax.Hub.Config({
 </script>
 
 
-
-    
 <!-- ------------------- main content ---------------------- -->
+<center>
+<h1>Week 44: From Decision Trees to Bagging methods</h1>
+</center>  <!-- document title -->
 
-
-
-<center><h1>Week 44: From Decision Trees to Bagging methods</h1></center>  <!-- document title -->
-
-<p>
 <!-- author(s): Morten Hjorth-Jensen -->
-
 <center>
 <b>Morten Hjorth-Jensen</b> [1, 2]
 </center>
-
-<p>
 <!-- institution(s) -->
+<center>
+[1] <b>Department of Physics, University of Oslo</b>
+</center>
+<center>
+[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b>
+</center>
+<br>
+<center>
+<h4>Nov 1, 2021</h4>
+</center> <!-- date -->
+<br>
 
-<center>[1] <b>Department of Physics, University of Oslo</b></center>
-<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
-<br>
-<p>
-<center><h4>Nov 5, 2020</h4></center> <!-- date -->
-<br>
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
-<h2 id="___sec0">Overview of week 44 </h2>
+<h2 id="overview-of-week-44">Overview of week 44 </h2>
 
 <ul>
-<li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober29.mp4?vrtx=view-as-webpage" target="_blank">Thursday: Wrapping up PCA from last week and basics of decision trees, classification and regression algorithms with video of lecture</a></li> 
-<li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober30.mp4?vrtx=view-as-webpage" target="_blank">Friday: Decision trees, voting models and bagging with video of lecture</a></li> 
+<li> Thursday: Wrapping up PCA from last week and basics of decision trees, classification and regression algorithms</li>
+<li> Friday: Decision trees, voting models and bagging</li>
 </ul>
+<p>Geron's chapter 6 covers decision trees while ensemble models, voting and bagging are discussed in chapter 7. See also lecture from <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf" target="_blank">STK-IN4300, lecture 7</a>. Chapter 9.2 of Hastie et al contains also a good discussion.</p>
 
-Geron's chapter 6 covers decision trees while ensemble models, voting and bagging are discussed in chapter 7. See also lecture from <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf" target="_blank">STK-IN4300, lecture 7</a>. Chapter 9.2 of Hastie et al contains also a good discussion.
-
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="thursday">Thursday </h2>
 
-<h2 id="___sec1">Thursday </h2>
+<p>Decision trees and wrapping up PCA.</p>
 
-<p>
-Decision trees and wrapping up PCA.
-
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="decision-trees-overarching-aims">Decision trees, overarching aims  </h2>
 
-<h2 id="___sec2">Decision trees, overarching aims  </h2>
-
-<p>
-We start here with the most basic algorithm, the so-called decision
+<p>We start here with the most basic algorithm, the so-called decision
 tree. With this basic algorithm we can in turn build more complex
 networks, spanning from homogeneous and heterogenous forests (bagging,
 random forests and more) to one of the most popular supervised
 algorithms nowadays, the extreme gradient boosting, or just
 XGBoost. But let us start with the simplest possible ingredient.
+</p>
 
-<p>
-Decision trees are supervised learning algorithms used for both,
+<p>Decision trees are supervised learning algorithms used for both,
 classification and regression tasks.
+</p>
 
-<p>
-The main idea of decision trees
+<p>The main idea of decision trees
 is to find those descriptive features which contain the most
 <b>information</b> regarding the target feature and then split the dataset
 along the values of these features such that the target feature values
 for the resulting underlying datasets are as pure as possible.
+</p>
 
-<p>
-The descriptive features which reproduce best the target/output features are normally  said
+<p>The descriptive features which reproduce best the target/output features are normally  said
 to be the most informative ones. The process of finding the <b>most
 informative</b> feature is done until we accomplish a stopping criteria
-where we then finally end up in so called <b>leaf nodes</b>.
+where we then finally end up in so called <b>leaf nodes</b>. 
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="basics-of-a-tree">Basics of a tree </h2>
 
-<h2 id="___sec3">Basics of a tree </h2>
-
-<p>
-A decision tree is typically divided into a <b>root node</b>, the <b>interior nodes</b>,
+<p>A decision tree is typically divided into a <b>root node</b>, the <b>interior nodes</b>,
 and the final <b>leaf nodes</b> or just <b>leaves</b>. These entities are then connected by so-called <b>branches</b>.
+</p>
 
-<p>
-The leaf nodes
+<p>The leaf nodes
 contain the predictions we will make for new query instances presented
 to our trained model. This is possible since the model has 
 learned the underlying structure of the training data and hence can,
 given some assumptions, make predictions about the target feature value
 (class) of unseen query instances.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="a-sketch-of-a-tree-regression-problem">A Sketch of a Tree, Regression problem  </h2>
 
-<h2 id="___sec4">A Sketch of a Tree, Regression problem  </h2>
-
-<p>
 <!-- FIGURE: [DataFiles/Regsimpletree.png, width=600 frac=0.8] -->
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="a-sketch-of-a-tree-classification-problem">A Sketch of a Tree, Classification  problem  </h2>
 
-<h2 id="___sec5">A Sketch of a Tree, Classification  problem  </h2>
-
-<p>
 <!-- FIGURE: [DataFiles/Classimpletree.png, width=600 frac=0.8] -->
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="a-typical-decision-tree-with-its-pertinent-jargon-classification-problem">A typical Decision Tree with its pertinent Jargon, Classification Problem </h2>
 
-<h2 id="___sec6">A typical Decision Tree with its pertinent Jargon, Classification Problem </h2>
+<br/><br/>
+<center>
+<p><img src="DataFiles/cancer.png" width="600" align="bottom"></p>
+</center>
+<br/><br/>
 
-<p>
-<br /><br /><center><p><img src="DataFiles/cancer.png" align="bottom" width=600></p></center><br /><br />
+<p>This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using <b>Scikit-Learn</b>'s decision tree classifier. Here we have used the so-called <b>gini</b> index (see below) to split the various branches.</p>
 
-<p>
-This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using <b>Scikit-Learn</b>'s decision tree classifier. Here we have used the so-called <b>gini</b> index (see below) to split the various branches.
-
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="general-features">General Features </h2>
 
-<h2 id="___sec7">General Features </h2>
-
-<p>
-The overarching approach to decision trees is a top-down approach.
+<p>The overarching approach to decision trees is a top-down approach.</p>
 
 <ul>
 <li> A leaf provides the classification of a given instance.</li>
@@ -290,18 +406,16 @@ The overarching approach to decision trees is a top-down approach.
 <li> A branch corresponds to a possible values of an attribute.</li>
 <li> An instance is classified by starting at the root node of the tree, testing the attribute specified by this node, then moving down the tree branch corresponding to the value of the attribute in the given example.</li>
 </ul>
-
-This process is then repeated for the subtree rooted at the new
+<p>This process is then repeated for the subtree rooted at the new
 node.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="how-do-we-set-it-up">How do we set it up? </h2>
 
-<h2 id="___sec8">How do we set it up? </h2>
-
-<p>
-In simplified terms, the process of training a decision tree and
+<p>In simplified terms, the process of training a decision tree and
 predicting the target features of query instances is as follows:
+</p>
 
 <ol>
 <li> Present a dataset containing of a number of training instances characterized by a number of descriptive features and a target feature</li>
@@ -309,17 +423,18 @@ predicting the target features of query instances is as follows:
 <li> Grow the tree until we accomplish a stopping criteria create leaf nodes which represent the <em>predictions</em> we want to make for new query instances</li>
 <li> Show query instances to the tree and run down the tree until we arrive at leaf nodes</li>
 </ol>
+<p>Then we are essentially done!</p>
 
-Then we are essentially done!
-
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
-<h2 id="___sec9">Decision trees and Regression  </h2>
-<p>
+<h2 id="decision-trees-and-regression">Decision trees and Regression  </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> PolynomialFeatures
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.linear_model</span> <span style="color: #008000; font-weight: bold">import</span> LinearRegression
@@ -407,96 +522,102 @@ plt<span style="color: #666666">.</span>ylabel(<span style="color: #BA2121">&quo
 plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&quot;Decision Tree Regression&quot;</span>)
 plt<span style="color: #666666">.</span>legend()
 plt<span style="color: #666666">.</span>show()
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="building-a-tree-regression">Building a tree, regression </h2>
 
-<h2 id="___sec10">Building a tree, regression </h2>
-
-<p>
-There are mainly two steps
-
+<p>There are mainly two steps</p>
 <ol>
 <li> We split the predictor space (the set of possible values \( x_1,x_2,\dots, x_p \)) into \( J \) distinct and non-non-overlapping regions, \( R_1,R_2,\dots,R_J \).</li>  
 <li> For every observation that falls into the region \( R_j \) , we make the same prediction, which is simply the mean of the response values for the training observations in \( R_j \).</li>
 </ol>
-
-How do we construct the regions \( R_1,\dots,R_J \)?  In theory, the
+<p>How do we construct the regions \( R_1,\dots,R_J \)?  In theory, the
 regions could have any shape. However, we choose to divide the
 predictor space into high-dimensional rectangles, or boxes, for
 simplicity and for ease of interpretation of the resulting predictive
 model. The goal is to find boxes \( R_1,\dots,R_J \) that minimize the
 MSE, given by
+</p>
 
 $$
 \sum_{j=1}^J\sum_{i\in R_j}(y_i-\overline{y}_{R_j})^2,
 $$
 
-<p>
-where \( \overline{y}_{R_j} \)  is the mean response for the training observations 
-within box \( j \).
+<p>where \( \overline{y}_{R_j} \)  is the mean response for the training observations 
+within box \( j \). 
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="a-top-down-approach-recursive-binary-splitting">A top-down approach, recursive binary splitting </h2>
 
-<h2 id="___sec11">A top-down approach, recursive binary splitting </h2>
-
-<p>
-Unfortunately, it is computationally infeasible to consider every
+<p>Unfortunately, it is computationally infeasible to consider every
 possible partition of the feature space into \( J \) boxes.  The common
 strategy is to take a top-down approach
+</p>
 
-<p>
-The approach is top-down because it begins at the top of the tree (all
+<p>The approach is top-down because it begins at the top of the tree (all
 observations belong to a single region) and then successively splits
 the predictor space; each split is indicated via two new branches
 further down on the tree. It is greedy because at each step of the
 tree-building process, the best split is made at that particular step,
 rather than looking ahead and picking a split that will lead to a
 better tree in some future step.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="making-a-tree">Making a tree </h2>
 
-<h2 id="___sec12">Making a tree </h2>
-
-<p>
-In order to implement the recursive binary splitting we start by selecting
+<p>In order to implement the recursive binary splitting we start by selecting
 the predictor \( x_j \) and a cutpoint \( s \) that splits the predictor space into two regions \( R_1 \) and \( R_2 \)
+</p>
 $$
 \left\{X\vert x_j < s\right\},
 $$
 
-and
+<p>and</p>
 $$
 \left\{X\vert x_j \geq s\right\},
 $$
 
-so that we obtain the lowest MSE, that is
+<p>so that we obtain the lowest MSE, that is</p>
 $$
 \sum_{i:x_i\in R_j}(y_i-\overline{y}_{R_1})^2+\sum_{i:x_i\in R_2}(y_i-\overline{y}_{R_2})^2,
 $$
 
-<p>
-which we want to minimize by considering all predictors
+<p>which we want to minimize by considering all predictors
 \( x_1,x_2,\dots,x_p \).  We consider also all possible values of \( s \) for
 each predictor. These values could be determined by randomly assigned
 numbers or by starting at the midpoint and then proceed till we find
 an optimal value.
+</p>
 
-<p>
-For any \( j \) and \( s \), we define the pair of half-planes where
+<p>For any \( j \) and \( s \), we define the pair of half-planes where
 \( \overline{y}_{R_1} \) is the mean response for the training
 observations in \( R_1(j,s) \), and \( \overline{y}_{R_2} \) is the mean
 response for the training observations in \( R_2(j,s) \).
+</p>
 
-<p>
-Finding the values of \( j \) and \( s \) that minimize the above equation can be
+<p>Finding the values of \( j \) and \( s \) that minimize the above equation can be
 done quite quickly, especially when the number of features \( p \) is not
 too large.
+</p>
 
-<p>
-Next, we repeat the process, looking
+<p>Next, we repeat the process, looking
 for the best predictor and best cutpoint in order to split the data
 further so as to minimize the MSE within each of the resulting
 regions. However, this time, instead of splitting the entire predictor
@@ -505,95 +626,83 @@ have three regions. Again, we look to split one of these three regions
 further, so as to minimize the MSE. The process continues until a
 stopping criterion is reached; for instance, we may continue until no
 region contains more than five observations.
+</p>
 
-<p>
 <!-- !split  -->
+<h2 id="pruning-the-tree">Pruning the tree </h2>
 
-<h2 id="___sec13">Pruning the tree </h2>
-
-<p>
-The above procedure is rather straightforward, but leads often to
+<p>The above procedure is rather straightforward, but leads often to
 overfitting and unnecessarily large and complicated trees. The basic
 idea is to grow a large tree \( T_0 \) and then prune it back in order to
 obtain a subtree. A smaller tree with fewer splits (fewer regions) can
 lead to smaller variance and better interpretation at the cost of a
 little more bias.
+</p>
 
-<p>
-The so-called Cost complexity pruning algorithm gives us a
+<p>The so-called Cost complexity pruning algorithm gives us a
 way to do just this. Rather than considering every possible subtree,
 we consider a sequence of trees indexed by a nonnegative tuning
 parameter \( \alpha \).
+</p>
 
-<p>
-Read more at the following <a href="https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py" target="_blank">Scikit-Learn link on pruning</a>.
+<p>Read more at the following <a href="https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py" target="_blank">Scikit-Learn link on pruning</a>.</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="cost-complexity-pruning">Cost complexity pruning </h2>
 
-<h2 id="___sec14">Cost complexity pruning </h2>
-
-<p>
-For each value of \( \alpha \)  there corresponds a subtree \( T \in T_0 \) such that
+<p>For each value of \( \alpha \)  there corresponds a subtree \( T \in T_0 \) such that</p>
 $$
 \sum_{m=1}^{\overline{T}}\sum_{i:x_i\in R_m}(y_i-\overline{y}_{R_m})^2+\alpha\overline{T},
 $$
 
-is as small as possible. Here \( \overline{T} \) is 
+<p>is as small as possible. Here \( \overline{T} \) is 
 the number of terminal nodes of the tree \( T \) , \( R_m \) is the
 rectangle (i.e. the subset of predictor space)  corresponding to the \( m \)-th terminal node.
+</p>
 
-<p>
-The tuning parameter \( \alpha \) controls a trade-off between the subtree&#8217;s
+<p>The tuning parameter \( \alpha \) controls a trade-off between the subtree&#8217;s
 complexity and its fit to the training data. When \( \alpha = 0 \), then the
 subtree \( T \) will simply equal \( T_0 \), 
 because then the above equation just measures the
 training error. 
 However, as \( \alpha \) increases, there is a price to pay for
 having a tree with many terminal nodes. The above equation will
-tend to be minimized for a smaller subtree.
+tend to be minimized for a smaller subtree. 
+</p>
 
-<p>
-It turns out that as we increase \( \alpha \) from zero
+<p>It turns out that as we increase \( \alpha \) from zero
 branches get pruned from the tree in a nested and predictable fashion,
 so obtaining the whole sequence of subtrees as a function of \( \alpha \) is
 easy. We can select a value of \( \alpha \) using a validation set or using
 cross-validation. We then return to the full data set and obtain the
-subtree corresponding to \( \alpha \).
+subtree corresponding to \( \alpha \). 
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="schematic-regression-procedure">Schematic Regression Procedure </h2>
 
-<h2 id="___sec15">Schematic Regression Procedure </h2>
-
-<p>
 <div class="alert alert-block alert-block alert-text-normal">
-<b>Building a Regression Tree.</b>
+<b>Building a Regression Tree</b>
 <p>
 
 <ol>
 <li> Use recursive binary splitting to grow a large tree on the training data, stopping only when each terminal node has fewer than some minimum number of observations.</li>
 <li> Apply cost complexity pruning to the large tree in order to obtain a sequence of best subtrees, as a function of \( \alpha \).</li>
 <li> Use for example \( K \)-fold cross-validation to choose \( \alpha \). Divide the training observations into \( K \) folds. For each \( k=1,2,\dots,K \) we:</li> 
-
 <ul>
   <li> repeat steps 1 and 2 on all but the \( k \)-th fold of the training data.</li> 
   <li> Then we valuate the mean squared prediction error on the data in the left-out \( k \)-th fold, as a function of \( \alpha \).</li>
   <li> Finally  we average the results for each value of \( \alpha \), and pick \( \alpha \) to minimize the average error.</li>
 </ul>
-
 <li> Return the subtree from Step 2 that corresponds to the chosen value of \( \alpha \).</li> 
 </ol>
 </div>
 
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="a-classification-tree">A Classification Tree </h2>
 
-<h2 id="___sec16">A Classification Tree </h2>
-
-<p>
-A classification tree is very similar to a regression tree, except
+<p>A classification tree is very similar to a regression tree, except
 that it is used to predict a qualitative response rather than a
 quantitative one. Recall that for a regression tree, the predicted
 response for an observation is given by the mean response of the
@@ -604,15 +713,13 @@ in the region to which it belongs. In interpreting the results of a
 classification tree, we are often interested not only in the class
 prediction corresponding to a particular terminal node region, but
 also in the class proportions among the training observations that
-fall into that region.
+fall into that region.  
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="growing-a-classification-tree">Growing a classification tree </h2>
 
-<h2 id="___sec17">Growing a classification tree </h2>
-
-<p>
-The task of growing a
+<p>The task of growing a
 classification tree is quite similar to the task of growing a
 regression tree. Just as in the regression setting, we use recursive
 binary splitting to grow a classification tree. However, in the
@@ -622,72 +729,69 @@ error rate</b>. Since we plan to assign an observation in a given region
 to the most commonly occurring error rate class of training
 observations in that region, the classification error rate is simply
 the fraction of the training observations in that region that do not
-belong to the most common class.
+belong to the most common class. 
+</p>
 
-<p>
-When building a classification tree, either the Gini index or the
+<p>When building a classification tree, either the Gini index or the
 entropy are typically used to evaluate the quality of a particular
 split, since these two approaches are more sensitive to node purity
-than is the classification error rate.
+than is the classification error rate. 
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="classification-tree-how-to-split-nodes">Classification tree, how to split nodes </h2>
 
-<h2 id="___sec18">Classification tree, how to split nodes </h2>
-
-<p>
-If our targets are the outcome of a classification process that takes
+<p>If our targets are the outcome of a classification process that takes
 for example \( k=1,2,\dots,K \) values, the only thing we need to think of
 is to set up the splitting criteria for each node.
+</p>
 
-<p>
-We define a PDF \( p_{mk} \) that represents the number of observations of
+<p>We define a PDF \( p_{mk} \) that represents the number of observations of
 a class \( k \) in a region \( R_m \) with \( N_m \) observations. We represent
 this likelihood function in terms of the proportion \( I(y_i=k) \) of
 observations of this class in the region \( R_m \) as
+</p>
 
 $$
 p_{mk} = \frac{1}{N_m}\sum_{x_i\in R_m}I(y_i=k).
 $$
 
-<p>
-We let \( p_{mk} \) represent the majority class of observations in region
+<p>We let \( p_{mk} \) represent the majority class of observations in region
 \( m \). The three most common ways of splitting a node are given by
+</p>
 
 <ul>
 <li> Misclassification error</li> 
 </ul>
-
 $$
 p_{mk} = \frac{1}{N_m}\sum_{x_i\in R_m}I(y_i\ne k) = 1-p_{mk}.
 $$
 
-
 <ul>
 <li> Gini index \( g \)</li>
 </ul>
-
 $$
 g = \sum_{k=1}^K p_{mk}(1-p_{mk}).
 $$
 
-
 <ul>
 <li> Information entropy or just entropy \( s \)</li>
 </ul>
-
 $$
 s = -\sum_{k=1}^K p_{mk}\log{p_{mk}}.
 $$
 
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec19">Visualizing the Tree, Classification </h2>
-<p>
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="visualizing-the-tree-classification">Visualizing the Tree, Classification </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">os</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">os</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.datasets</span> <span style="color: #008000; font-weight: bold">import</span> load_breast_cancer
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeClassifier
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
@@ -720,15 +824,32 @@ export_graphviz(
 )
 cmd <span style="color: #666666">=</span> <span style="color: #BA2121">&#39;dot -Tpng DataFiles/cancer.dot -o DataFiles/cancer.png&#39;</span>
 os<span style="color: #666666">.</span>system(cmd)
-</pre></div>
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 
-<h2 id="___sec20">Visualizing the Tree, The Moons  </h2>
-<p>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="visualizing-the-tree-the-moons">Visualizing the Tree, The Moons  </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #408080; font-style: italic"># Common imports</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #408080; font-style: italic"># Common imports</span>
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span>  train_test_split 
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeClassifier
@@ -752,39 +873,72 @@ export_graphviz(
 )
 cmd <span style="color: #666666">=</span> <span style="color: #BA2121">&#39;dot -Tpng DataFiles/moons.dot -o DataFiles/moons.png&#39;</span>
 os<span style="color: #666666">.</span>system(cmd)
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="other-ways-of-visualizing-the-trees">Other ways of visualizing the trees </h2>
 
-<h2 id="___sec21">Other ways of visualizing the trees </h2>
+<p><b>Scikit-Learn</b> has also another way to visualize the trees which is very useful, here with the Iris data.</p>
 
-<p>
-<b>Scikit-Learn</b> has also another way to visualize the trees which is very useful, here with the Iris data.
-
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.datasets</span> <span style="color: #008000; font-weight: bold">import</span> load_iris
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.datasets</span> <span style="color: #008000; font-weight: bold">import</span> load_iris
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn</span> <span style="color: #008000; font-weight: bold">import</span> tree
 X, y <span style="color: #666666">=</span> load_iris(return_X_y<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>)
 tree_clf <span style="color: #666666">=</span> tree<span style="color: #666666">.</span>DecisionTreeClassifier()
 tree_clf <span style="color: #666666">=</span> tree_clf<span style="color: #666666">.</span>fit(X, y)
 <span style="color: #408080; font-style: italic"># and then plot the tree</span>
 tree<span style="color: #666666">.</span>plot_tree(tree_clf) 
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="printing-out-as-text">Printing out as text </h2>
 
-<h2 id="___sec22">Printing out as text </h2>
-
-<p>
-Alternatively, the tree can also be exported in textual format with the function exporttext.
+<p>Alternatively, the tree can also be exported in textual format with the function exporttext.
 This method doesn&#8217;t require the installation of external libraries and is more compact:
+</p>
 
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.datasets</span> <span style="color: #008000; font-weight: bold">import</span> load_iris
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.datasets</span> <span style="color: #008000; font-weight: bold">import</span> load_iris
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeClassifier
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> export_text
 iris <span style="color: #666666">=</span> load_iris()
@@ -792,87 +946,92 @@ decision_tree <span style="color: #666666">=</span> DecisionTreeClassifier(rando
 decision_tree <span style="color: #666666">=</span> decision_tree<span style="color: #666666">.</span>fit(iris<span style="color: #666666">.</span>data, iris<span style="color: #666666">.</span>target)
 r <span style="color: #666666">=</span> export_text(decision_tree, feature_names<span style="color: #666666">=</span>iris[<span style="color: #BA2121">&#39;feature_names&#39;</span>])
 <span style="color: #008000">print</span>(r)
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="algorithms-for-setting-up-decision-trees">Algorithms for Setting up Decision Trees </h2>
 
-<h2 id="___sec23">Algorithms for Setting up Decision Trees </h2>
-
-<p>
-Two algorithms stand out in the set up of decision trees:
-
+<p>Two algorithms stand out in the set up of decision trees:</p>
 <ol>
 <li> The CART (Classification And Regression Tree) algorithm for both classification and regression</li>
 <li> The ID3 algorithm based on the computation of the information gain for classification</li>
 </ol>
-
-We discuss both algorithms with applications here. The popular library
+<p>We discuss both algorithms with applications here. The popular library
 <b>Scikit-Learn</b> uses the CART algorithm. For classification problems
 you can use either the <b>gini</b> index or the <b>entropy</b> to split a tree
 in two branches.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="the-cart-algorithm-for-classification">The CART algorithm for Classification </h2>
 
-<h2 id="___sec24">The CART algorithm for Classification </h2>
-
-<p>
-For classification, the CART algorithm splits the data set in two subsets using a single feature \( k \) and a threshold \( t_k \).
+<p>For classification, the CART algorithm splits the data set in two subsets using a single feature \( k \) and a threshold \( t_k \).
 This could be for example a threshold set by a number below a certain circumference of a malign tumor.
+</p>
 
-<p>
-How do we find these two quantities?
+<p>How do we find these two quantities?
 We search for the pair \( (k,t_k) \) that produces the purest subset using for example the <b>gini</b> factor \( G \).
 The cost function it tries to minimize is then
+</p>
 $$
 C(k,t_k) = \frac{m_{\mathrm{left}}}{m}G_{\mathrm{left}}+ \frac{m_{\mathrm{right}}}{m}G_{\mathrm{right}},
 $$
 
-where \( G_{\mathrm{left/right}} \) measures the impurity of the left/right subset  and \( m_{\mathrm{left/right}} \)
+<p>where \( G_{\mathrm{left/right}} \) measures the impurity of the left/right subset  and \( m_{\mathrm{left/right}} \)
  is the number of instances in the left/right subset
+</p>
 
-<p>
-Once it has successfully split the training set in two, it splits the subsets using the same logic, then the subsubsets
+<p>Once it has successfully split the training set in two, it splits the subsets using the same logic, then the subsubsets
 and so on, recursively. It stops recursing once it reaches the maximum depth (defined by the
 \( max\_depth \) hyperparameter), or if it cannot find a split that will reduce impurity. A few other
 hyperparameters control additional stopping conditions such as the \( min\_samples\_split \),
 \( min\_samples\_leaf \), \( min\_weight\_fraction\_leaf \), and \( max\_leaf\_nodes \).
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="the-cart-algorithm-for-regression">The CART algorithm for Regression </h2>
 
-<h2 id="___sec25">The CART algorithm for Regression </h2>
-
-<p>
-The CART algorithm for regression works is similar to the one for classification except that instead of trying to split the
+<p>The CART algorithm for regression works is similar to the one for classification except that instead of trying to split the
 training set in a way that minimizes say the <b>gini</b> or <b>entropy</b> impurity, it now tries to split the training set in a way that minimizes our well-known mean-squared error (MSE). The cost function is now
+</p>
 $$
 C(k,t_k) = \frac{m_{\mathrm{left}}}{m}\mathrm{MSE}_{\mathrm{left}}+ \frac{m_{\mathrm{right}}}{m}\mathrm{MSE}_{\mathrm{right}}.
 $$
 
-Here the MSE for a specific node is defined as
+<p>Here the MSE for a specific node is defined as</p>
 $$
 \mathrm{MSE}_{\mathrm{node}}=\frac{1}{m_\mathrm{node}}\sum_{i\in \mathrm{node}}(\overline{y}_{\mathrm{node}}-y_i)^2,
 $$
 
-with
+<p>with</p>
 $$
 \overline{y}_{\mathrm{node}}=\frac{1}{m_\mathrm{node}}\sum_{i\in \mathrm{node}}y_i,
 $$
 
-the mean value of all observations in a specific node.
+<p>the mean value of all observations in a specific node.</p>
 
-<p>
-Without any regularization, the regression task for decision trees, 
+<p>Without any regularization, the regression task for decision trees, 
 just like for classification tasks, is  prone to overfitting.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="computing-the-gini-index">Computing the Gini index </h2>
 
-<h2 id="___sec26">Computing the Gini index </h2>
-
-<p>
-The example we will look at is a classical one in many Machine
+<p>The example we will look at is a classical one in many Machine
 Learning applications. Based on various meteorological features, we
 have several so-called attributes which decide whether we at the end
 will do some outdoor activity like skiing, going for a bike ride etc
@@ -882,10 +1041,10 @@ etc.  The table here contains the feautures <b>outlook</b>, <b>temperature</b>,
 attributes for each feature are then sunny, overcast and rain for the
 outlook, hot, cold and mild for temperature, high and normal for
 humidity and weak and strong for wind.
+</p>
 
-<p>
-The table here summarizes the various attributes and
-<table border="1">
+<p>The table here summarizes the various attributes and</p>
+<table class="dotable" border="1">
 <thead>
 <tr><th align="center">Day</th> <th align="center">Outlook </th> <th align="center">Temperature</th> <th align="center">Humidity</th> <th align="center"> Wind </th> <th align="center">Ride</th> </tr>
 </thead>
@@ -906,15 +1065,18 @@ The table here summarizes the various attributes and
 <tr><td align="center">   14     </td> <td align="center">   Rain        </td> <td align="center">   Mild           </td> <td align="center">   High        </td> <td align="center">   Strong    </td> <td align="center">   0       </td> </tr>
 </tbody>
 </table>
-<p>
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="simple-python-code-to-read-in-data-and-perform-classification">Simple Python Code to read in Data and perform Classification </h2>
 
-<h2 id="___sec27">Simple Python Code to read in Data and perform Classification </h2>
-
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #408080; font-style: italic"># Common imports</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #408080; font-style: italic"># Common imports</span>
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">pandas</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">pd</span>
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
@@ -981,25 +1143,41 @@ export_graphviz(
 )
 cmd <span style="color: #666666">=</span> <span style="color: #BA2121">&#39;dot -Tpng DataFiles/cancer.dot -o DataFiles/cancer.png&#39;</span>
 os<span style="color: #666666">.</span>system(cmd)
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="computing-the-gini-factor">Computing the Gini Factor  </h2>
 
-<h2 id="___sec28">Computing the Gini Factor  </h2>
-
-<p>
-The above functions (gini, entropy and misclassification error) are
+<p>The above functions (gini, entropy and misclassification error) are
 important components of the so-called CART algorithm. We will discuss
 this algorithm below after we have discussed the information gain
 algorithm ID3.
+</p>
 
-<p>
-In the example here we have converted all our attributes into numerical values \( 0,1,2 \) etc.
+<p>In the example here we have converted all our attributes into numerical values \( 0,1,2 \) etc.</p>
 
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #408080; font-style: italic"># Split a dataset based on an attribute and an attribute value</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #408080; font-style: italic"># Split a dataset based on an attribute and an attribute value</span>
 <span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">test_split</span>(index, value, dataset):
 	left, right <span style="color: #666666">=</span> <span style="color: #008000">list</span>(), <span style="color: #008000">list</span>()
 	<span style="color: #008000; font-weight: bold">for</span> row <span style="color: #AA22FF; font-weight: bold">in</span> dataset:
@@ -1059,15 +1237,28 @@ dataset <span style="color: #666666">=</span> [[<span style="color: #666666">0</
 
 split <span style="color: #666666">=</span> get_split(dataset)
 <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;Split: [X</span><span style="color: #BB6688; font-weight: bold">%d</span><span style="color: #BA2121"> &lt; </span><span style="color: #BB6688; font-weight: bold">%.3f</span><span style="color: #BA2121">]&#39;</span> <span style="color: #666666">%</span> ((split[<span style="color: #BA2121">&#39;index&#39;</span>]<span style="color: #666666">+1</span>), split[<span style="color: #BA2121">&#39;value&#39;</span>]))
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="entropy-and-the-id3-algorithm">Entropy and the ID3 algorithm </h2>
 
-<h2 id="___sec29">Entropy and the ID3 algorithm </h2>
-
-<p>
-The ID3 algorithm  learns decision trees by constructing
+<p>The ID3 algorithm  learns decision trees by constructing
 them in a top down way, beginning with the question <b>which attribute should be tested at the root of the tree</b>?
+</p>
 
 <ol>
 <li> Each instance attribute is evaluated using a statistical test to determine how well it alone classifies the training examples.</li>
@@ -1077,33 +1268,34 @@ them in a top down way, beginning with the question <b>which attribute should be
 <li> The entire process is then repeated using the training examples associated with each descendant node to select the best attribute to test at that point in the tree.</li>
 <li> This forms a greedy search for an acceptable decision tree, in which the algorithm never backtracks to reconsider earlier choices.</li> 
 </ol>
-
-The ID3 algorithm selects which attribute to test at each node in the
+<p>The ID3 algorithm selects which attribute to test at each node in the
 tree.
+</p>
 
-<p>
-We would like to select the attribute that is most useful for classifying
+<p>We would like to select the attribute that is most useful for classifying
 examples.
+</p>
 
-<p>
-What is a good quantitative measure of the worth of an attribute?
+<p>What is a good quantitative measure of the worth of an attribute?</p>
 
-<p>
-Information gain measures how well a given attribute separates the
+<p>Information gain measures how well a given attribute separates the
 training examples according to their target classification.
+</p>
 
-<p>
-The ID3 algorithm uses this information gain measure to select among the candidate
+<p>The ID3 algorithm uses this information gain measure to select among the candidate
 attributes at each step while growing the tree.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
-<h2 id="___sec30">Cancer Data again now with Decision Trees and other Methods </h2>
-<p>
+<h2 id="cancer-data-again-now-with-decision-trees-and-other-methods">Cancer Data again now with Decision Trees and other Methods </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span>  train_test_split 
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.datasets</span> <span style="color: #008000; font-weight: bold">import</span> load_breast_cancer
@@ -1144,15 +1336,32 @@ svm<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
 <span style="color: #408080; font-style: italic"># Decision Trees</span>
 deep_tree_clf<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
 <span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Test set accuracy with Decision Trees and scaled data: </span><span style="color: #BB6688; font-weight: bold">{:.2f}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(deep_tree_clf<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
-</pre></div>
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 
-<h2 id="___sec31">Another example, the moons again </h2>
-<p>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="another-example-the-moons-again">Another example, the moons again </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">__future__</span> <span style="color: #008000; font-weight: bold">import</span> division, print_function, unicode_literals
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">__future__</span> <span style="color: #008000; font-weight: bold">import</span> division, print_function, unicode_literals
 
 <span style="color: #408080; font-style: italic"># Common imports</span>
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
@@ -1216,15 +1425,32 @@ plt<span style="color: #666666">.</span>subplot(<span style="color: #666666">122
 plot_decision_boundary(deep_tree_clf2, Xm, ym, axes<span style="color: #666666">=</span>[<span style="color: #666666">-1.5</span>, <span style="color: #666666">2.5</span>, <span style="color: #666666">-1</span>, <span style="color: #666666">1.5</span>], iris<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">False</span>)
 plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&quot;min_samples_leaf = </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(deep_tree_clf2<span style="color: #666666">.</span>min_samples_leaf), fontsize<span style="color: #666666">=14</span>)
 plt<span style="color: #666666">.</span>show()
-</pre></div>
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 
-<h2 id="___sec32">Playing around with regions </h2>
-<p>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="playing-around-with-regions">Playing around with regions </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>seed(<span style="color: #666666">6</span>)
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;">np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>seed(<span style="color: #666666">6</span>)
 Xs <span style="color: #666666">=</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>rand(<span style="color: #666666">100</span>, <span style="color: #666666">2</span>) <span style="color: #666666">-</span> <span style="color: #666666">0.5</span>
 ys <span style="color: #666666">=</span> (Xs[:, <span style="color: #666666">0</span>] <span style="color: #666666">&gt;</span> <span style="color: #666666">0</span>)<span style="color: #666666">.</span>astype(np<span style="color: #666666">.</span>float32) <span style="color: #666666">*</span> <span style="color: #666666">2</span>
 
@@ -1244,37 +1470,86 @@ plt<span style="color: #666666">.</span>subplot(<span style="color: #666666">122
 plot_decision_boundary(tree_clf_sr, Xsr, ys, axes<span style="color: #666666">=</span>[<span style="color: #666666">-0.7</span>, <span style="color: #666666">0.7</span>, <span style="color: #666666">-0.7</span>, <span style="color: #666666">0.7</span>], iris<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">False</span>)
 
 plt<span style="color: #666666">.</span>show()
-</pre></div>
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 
-<h2 id="___sec33">Regression trees </h2>
-<p>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="regression-trees">Regression trees </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #408080; font-style: italic"># Quadratic training set + noise</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #408080; font-style: italic"># Quadratic training set + noise</span>
 np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>seed(<span style="color: #666666">42</span>)
 m <span style="color: #666666">=</span> <span style="color: #666666">200</span>
 X <span style="color: #666666">=</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>rand(m, <span style="color: #666666">1</span>)
 y <span style="color: #666666">=</span> <span style="color: #666666">4</span> <span style="color: #666666">*</span> (X <span style="color: #666666">-</span> <span style="color: #666666">0.5</span>) <span style="color: #666666">**</span> <span style="color: #666666">2</span>
 y <span style="color: #666666">=</span> y <span style="color: #666666">+</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randn(m, <span style="color: #666666">1</span>) <span style="color: #666666">/</span> <span style="color: #666666">10</span>
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeRegressor
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeRegressor
 
 tree_reg <span style="color: #666666">=</span> DecisionTreeRegressor(max_depth<span style="color: #666666">=2</span>, random_state<span style="color: #666666">=42</span>)
 tree_reg<span style="color: #666666">.</span>fit(X, y)
-</pre></div>
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 
-<h2 id="___sec34">Final regressor code </h2>
-<p>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="final-regressor-code">Final regressor code </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeRegressor
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeRegressor
 
 tree_reg1 <span style="color: #666666">=</span> DecisionTreeRegressor(random_state<span style="color: #666666">=42</span>, max_depth<span style="color: #666666">=2</span>)
 tree_reg2 <span style="color: #666666">=</span> DecisionTreeRegressor(random_state<span style="color: #666666">=42</span>, max_depth<span style="color: #666666">=3</span>)
@@ -1312,11 +1587,26 @@ plt<span style="color: #666666">.</span>text(<span style="color: #666666">0.3</s
 plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&quot;max_depth=3&quot;</span>, fontsize<span style="color: #666666">=14</span>)
 
 plt<span style="color: #666666">.</span>show()
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>tree_reg1 <span style="color: #666666">=</span> DecisionTreeRegressor(random_state<span style="color: #666666">=42</span>)
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;">tree_reg1 <span style="color: #666666">=</span> DecisionTreeRegressor(random_state<span style="color: #666666">=42</span>)
 tree_reg2 <span style="color: #666666">=</span> DecisionTreeRegressor(random_state<span style="color: #666666">=42</span>, min_samples_leaf<span style="color: #666666">=10</span>)
 tree_reg1<span style="color: #666666">.</span>fit(X, y)
 tree_reg2<span style="color: #666666">.</span>fit(X, y)
@@ -1344,11 +1634,24 @@ plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">&quo
 plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&quot;min_samples_leaf=</span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(tree_reg2<span style="color: #666666">.</span>min_samples_leaf), fontsize<span style="color: #666666">=14</span>)
 
 plt<span style="color: #666666">.</span>show()
-</pre></div>
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 
-<h2 id="___sec35">Pros and cons of trees, pros </h2>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="pros-and-cons-of-trees-pros">Pros and cons of trees, pros </h2>
 
 <ul>
 <li> White box, easy to interpret model. Some people believe that decision trees more closely mirror human decision-making than do the regression and classification approaches discussed earlier (think of support vector machines)</li>
@@ -1359,10 +1662,8 @@ plt<span style="color: #666666">.</span>show()
 <li> Can model interactions between the different descriptive features</li>
 <li> Trees can be displayed graphically, and are easily interpreted even by a non-expert (especially if they are small)</li>
 </ul>
-
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
-<h2 id="___sec36">Disadvantages </h2>
+<h2 id="disadvantages">Disadvantages </h2>
 
 <ul>
 <li> Unfortunately, trees generally do not have the same level of predictive accuracy as some of the other regression and classification approaches</li>
@@ -1373,28 +1674,26 @@ plt<span style="color: #666666">.</span>show()
 <li> If the number of features is relatively large (high dimensional) and the number of instances is relatively low, the tree might overfit the data</li>
 <li> Features with many levels may be preferred over features with less levels since for them it is <em>more easy</em> to split the dataset such that the sub datasets only contain pure target feature values. This issue can be addressed by preferring for instance the information gain ratio as splitting criteria over information gain</li>
 </ul>
-
-However, by aggregating many decision trees, using methods like
+<p>However, by aggregating many decision trees, using methods like
 bagging, random forests, and boosting, the predictive performance of
 trees can be substantially improved.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="ensemble-methods-from-a-single-tree-to-many-trees-and-extreme-boosting-meet-the-jungle-of-methods">Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods </h2>
 
-<h2 id="___sec37">Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods </h2>
-
-<p>
-As stated above and seen in many of the examples discussed here about
+<p>As stated above and seen in many of the examples discussed here about
 a single decision tree, we often end up overfitting our training
 data. This normally means that we have a high variance. Can we reduce
 the variance of a statistical learning method?
+</p>
 
-<p>
-This leads us to a set of different methods that can combine different
+<p>This leads us to a set of different methods that can combine different
 machine learning algorithms or just use one of them to construct
 forests and jungles of trees, homogeneous ones or heterogenous
 ones. These methods are recognized by different names which we will
 try to explain here. These are
+</p>
 
 <ol>
 <li> Voting classifiers</li>
@@ -1402,50 +1701,45 @@ try to explain here. These are
 <li> Random forests</li>
 <li> Boosting methods, from adaptive to Extreme Gradient Boosting (XGBoost)</li>
 </ol>
+<p>We discuss these methods here.</p>
 
-We discuss these methods here.
-
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="an-overview-of-ensemble-methods">An Overview of Ensemble Methods  </h2>
 
-<h2 id="___sec38">An Overview of Ensemble Methods  </h2>
+<br/><br/>
+<center>
+<p><img src="DataFiles/ensembleoverview.png" width="600" align="bottom"></p>
+</center>
+<br/><br/>
 
-<p>
-<br /><br /><center><p><img src="DataFiles/ensembleoverview.png" align="bottom" width=600></p></center><br /><br />
-
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="bagging">Bagging </h2>
 
-<h2 id="___sec39">Bagging </h2>
-
-<p>
-The <b>plain</b> decision trees suffer from high
+<p>The <b>plain</b> decision trees suffer from high
 variance. This means that if we split the training data into two parts
 at random, and fit a decision tree to both halves, the results that we
 get could be quite different. In contrast, a procedure with low
 variance will yield similar results if applied repeatedly to distinct
 data sets; linear regression tends to have low variance, if the ratio
-of \( n \) to \( p \) is moderately large.
+of \( n \) to \( p \) is moderately large. 
+</p>
 
-<p>
-<b>Bootstrap aggregation</b>, or just <b>bagging</b>, is a
+<p><b>Bootstrap aggregation</b>, or just <b>bagging</b>, is a
 general-purpose procedure for reducing the variance of a statistical
-learning method.
+learning method. 
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="more-bagging">More bagging </h2>
 
-<h2 id="___sec40">More bagging </h2>
-
-<p>
-Bagging typically results in improved accuracy
+<p>Bagging typically results in improved accuracy
 over prediction using a single tree. Unfortunately, however, it can be
 difficult to interpret the resulting model. Recall that one of the
 advantages of decision trees is the attractive and easily interpreted
 diagram that results.
+</p>
 
-<p>
-However, when we bag a large number of trees, it is no longer
+<p>However, when we bag a large number of trees, it is no longer
 possible to represent the resulting statistical learning procedure
 using a single tree, and it is no longer clear which variables are
 most important to the procedure. Thus, bagging improves prediction
@@ -1460,15 +1754,18 @@ trees. A large value indicates an important predictor. Similarly, in
 the context of bagging classification trees, we can add up the total
 amount that the Gini index  is decreased by splits over a given
 predictor, averaged over all \( B \) trees.
+</p>
 
-<p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-
-<h2 id="___sec41">Simple Voting Example, head or tail </h2>
-<p>
+<h2 id="simple-voting-example-head-or-tail">Simple Voting Example, head or tail </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>heads_proba <span style="color: #666666">=</span> <span style="color: #666666">0.51</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;">heads_proba <span style="color: #666666">=</span> <span style="color: #666666">0.51</span>
 coin_tosses <span style="color: #666666">=</span> (np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>rand(<span style="color: #666666">10000</span>, <span style="color: #666666">10</span>) <span style="color: #666666">&lt;</span> heads_proba)<span style="color: #666666">.</span>astype(np<span style="color: #666666">.</span>int32)
 cumulative_heads_ratio <span style="color: #666666">=</span> np<span style="color: #666666">.</span>cumsum(coin_tosses, axis<span style="color: #666666">=0</span>) <span style="color: #666666">/</span> np<span style="color: #666666">.</span>arange(<span style="color: #666666">1</span>, <span style="color: #666666">10001</span>)<span style="color: #666666">.</span>reshape(<span style="color: #666666">-1</span>, <span style="color: #666666">1</span>)
 plt<span style="color: #666666">.</span>figure(figsize<span style="color: #666666">=</span>(<span style="color: #666666">8</span>,<span style="color: #666666">3.5</span>))
@@ -1481,15 +1778,32 @@ plt<span style="color: #666666">.</span>legend(loc<span style="color: #666666">=
 plt<span style="color: #666666">.</span>axis([<span style="color: #666666">0</span>, <span style="color: #666666">10000</span>, <span style="color: #666666">0.42</span>, <span style="color: #666666">0.58</span>])
 save_fig(<span style="color: #BA2121">&quot;votingsimple&quot;</span>)
 plt<span style="color: #666666">.</span>show()
-</pre></div>
-<p>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
 
-<h2 id="___sec42">Using the Voting Classifier </h2>
-<p>
+
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="using-the-voting-classifier">Using the Voting Classifier </h2>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.datasets</span> <span style="color: #008000; font-weight: bold">import</span> make_moons
 
 X, y <span style="color: #666666">=</span> make_moons(n_samples<span style="color: #666666">=500</span>, noise<span style="color: #666666">=0.30</span>, random_state<span style="color: #666666">=42</span>)
@@ -1532,16 +1846,33 @@ voting_clf<span style="color: #666666">.</span>fit(X_train, y_train)
     clf<span style="color: #666666">.</span>fit(X_train, y_train)
     y_pred <span style="color: #666666">=</span> clf<span style="color: #666666">.</span>predict(X_test)
     <span style="color: #008000">print</span>(clf<span style="color: #666666">.</span><span style="color: #19177C">__class__</span><span style="color: #666666">.</span><span style="color: #19177C">__name__</span>, accuracy_score(y_test, y_pred))
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="please-not-the-moons-again-voting-and-bagging">Please, not the moons again! Voting and Bagging </h2>
 
-<h2 id="___sec43">Please, not the moons again! Voting and Bagging </h2>
-
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.datasets</span> <span style="color: #008000; font-weight: bold">import</span> make_moons
 
 X, y <span style="color: #666666">=</span> make_moons(n_samples<span style="color: #666666">=500</span>, noise<span style="color: #666666">=0.30</span>, random_state<span style="color: #666666">=42</span>)
@@ -1559,21 +1890,51 @@ voting_clf <span style="color: #666666">=</span> VotingClassifier(
     estimators<span style="color: #666666">=</span>[(<span style="color: #BA2121">&#39;lr&#39;</span>, log_clf), (<span style="color: #BA2121">&#39;rf&#39;</span>, rnd_clf), (<span style="color: #BA2121">&#39;svc&#39;</span>, svm_clf)],
     voting<span style="color: #666666">=</span><span style="color: #BA2121">&#39;hard&#39;</span>)
 voting_clf<span style="color: #666666">.</span>fit(X_train, y_train)
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.metrics</span> <span style="color: #008000; font-weight: bold">import</span> accuracy_score
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.metrics</span> <span style="color: #008000; font-weight: bold">import</span> accuracy_score
 
 <span style="color: #008000; font-weight: bold">for</span> clf <span style="color: #AA22FF; font-weight: bold">in</span> (log_clf, rnd_clf, svm_clf, voting_clf):
     clf<span style="color: #666666">.</span>fit(X_train, y_train)
     y_pred <span style="color: #666666">=</span> clf<span style="color: #666666">.</span>predict(X_test)
     <span style="color: #008000">print</span>(clf<span style="color: #666666">.</span><span style="color: #19177C">__class__</span><span style="color: #666666">.</span><span style="color: #19177C">__name__</span>, accuracy_score(y_test, y_pred))
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>log_clf <span style="color: #666666">=</span> LogisticRegression(random_state<span style="color: #666666">=42</span>)
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;">log_clf <span style="color: #666666">=</span> LogisticRegression(random_state<span style="color: #666666">=42</span>)
 rnd_clf <span style="color: #666666">=</span> RandomForestClassifier(random_state<span style="color: #666666">=42</span>)
 svm_clf <span style="color: #666666">=</span> SVC(probability<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>, random_state<span style="color: #666666">=42</span>)
 
@@ -1581,26 +1942,58 @@ voting_clf <span style="color: #666666">=</span> VotingClassifier(
     estimators<span style="color: #666666">=</span>[(<span style="color: #BA2121">&#39;lr&#39;</span>, log_clf), (<span style="color: #BA2121">&#39;rf&#39;</span>, rnd_clf), (<span style="color: #BA2121">&#39;svc&#39;</span>, svm_clf)],
     voting<span style="color: #666666">=</span><span style="color: #BA2121">&#39;soft&#39;</span>)
 voting_clf<span style="color: #666666">.</span>fit(X_train, y_train)
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.metrics</span> <span style="color: #008000; font-weight: bold">import</span> accuracy_score
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.metrics</span> <span style="color: #008000; font-weight: bold">import</span> accuracy_score
 
 <span style="color: #008000; font-weight: bold">for</span> clf <span style="color: #AA22FF; font-weight: bold">in</span> (log_clf, rnd_clf, svm_clf, voting_clf):
     clf<span style="color: #666666">.</span>fit(X_train, y_train)
     y_pred <span style="color: #666666">=</span> clf<span style="color: #666666">.</span>predict(X_test)
     <span style="color: #008000">print</span>(clf<span style="color: #666666">.</span><span style="color: #19177C">__class__</span><span style="color: #666666">.</span><span style="color: #19177C">__name__</span>, accuracy_score(y_test, y_pred))
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="bagging-examples">Bagging Examples  </h2>
 
-<h2 id="___sec44">Bagging Examples  </h2>
-
-<p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> BaggingClassifier
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> BaggingClassifier
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeClassifier
 
 bag_clf <span style="color: #666666">=</span> BaggingClassifier(
@@ -1608,25 +2001,70 @@ bag_clf <span style="color: #666666">=</span> BaggingClassifier(
     max_samples<span style="color: #666666">=100</span>, bootstrap<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>, n_jobs<span style="color: #666666">=-1</span>, random_state<span style="color: #666666">=42</span>)
 bag_clf<span style="color: #666666">.</span>fit(X_train, y_train)
 y_pred <span style="color: #666666">=</span> bag_clf<span style="color: #666666">.</span>predict(X_test)
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.metrics</span> <span style="color: #008000; font-weight: bold">import</span> accuracy_score
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.metrics</span> <span style="color: #008000; font-weight: bold">import</span> accuracy_score
 <span style="color: #008000">print</span>(accuracy_score(y_test, y_pred))
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>tree_clf <span style="color: #666666">=</span> DecisionTreeClassifier(random_state<span style="color: #666666">=42</span>)
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;">tree_clf <span style="color: #666666">=</span> DecisionTreeClassifier(random_state<span style="color: #666666">=42</span>)
 tree_clf<span style="color: #666666">.</span>fit(X_train, y_train)
 y_pred_tree <span style="color: #666666">=</span> tree_clf<span style="color: #666666">.</span>predict(X_test)
 <span style="color: #008000">print</span>(accuracy_score(y_test, y_pred_tree))
-</pre></div>
-<p>
-
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">matplotlib.colors</span> <span style="color: #008000; font-weight: bold">import</span> ListedColormap
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">matplotlib.colors</span> <span style="color: #008000; font-weight: bold">import</span> ListedColormap
 
 <span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">plot_decision_boundary</span>(clf, X, y, axes<span style="color: #666666">=</span>[<span style="color: #666666">-1.5</span>, <span style="color: #666666">2.5</span>, <span style="color: #666666">-1</span>, <span style="color: #666666">1.5</span>], alpha<span style="color: #666666">=0.5</span>, contour<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>):
     x1s <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linspace(axes[<span style="color: #666666">0</span>], axes[<span style="color: #666666">1</span>], <span style="color: #666666">100</span>)
@@ -1653,19 +2091,36 @@ plot_decision_boundary(bag_clf, X, y)
 plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&quot;Decision Trees with Bagging&quot;</span>, fontsize<span style="color: #666666">=14</span>)
 save_fig(<span style="color: #BA2121">&quot;baggingtree&quot;</span>)
 plt<span style="color: #666666">.</span>show()
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="making-your-own-bootstrap-changing-the-level-of-the-decision-tree">Making your own Bootstrap: Changing the Level of the Decision Tree </h2>
 
-<h2 id="___sec45">Making your own Bootstrap: Changing the Level of the Decision Tree </h2>
-
-<p>
-Let us bring up our good old boostrap example from the linear regression lectures. We change the linerar regression algorithm with
+<p>Let us bring up our good old boostrap example from the linear regression lectures. We change the linerar regression algorithm with
 a decision tree wth different depths and perform a bootstrap aggregate (in this case we perform as many bootstraps as data points \( n \)).
-<p>
+</p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
-<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
+<div class="cell border-box-sizing code_cell rendered">
+  <div class="input">
+    <div class="inner_cell">
+      <div class="input_area">
+        <div class="highlight" style="background: #f8f8f8">
+  <pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.pipeline</span> <span style="color: #008000; font-weight: bold">import</span> make_pipeline
@@ -1722,18 +2177,26 @@ plt<span style="color: #666666">.</span>plot(polydegree, variance, label<span st
 plt<span style="color: #666666">.</span>legend()
 save_fig(<span style="color: #BA2121">&quot;baggingboot&quot;</span>)
 plt<span style="color: #666666">.</span>show()
-</pre></div>
-<p>
+</pre>
+</div>
+      </div>
+    </div>
+  </div>
+  <div class="output_wrapper">
+    <div class="output">
+      <div class="output_area">
+        <div class="output_subarea output_stream output_stdout output_text">          
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+
 
 <!-- ------------------- end of main content --------------- -->
-
-
 <center style="font-size:80%">
-<!-- copyright --> &copy; 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
+<!-- copyright --> &copy; 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
 </center>
-
-
 </body>
 </html>
-    
 
diff --git a/doc/pub/week44/ipynb/ipynb-week44-src.tar.gz b/doc/pub/week44/ipynb/ipynb-week44-src.tar.gz
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diff --git a/doc/pub/week44/ipynb/week44.ipynb b/doc/pub/week44/ipynb/week44.ipynb
index 47d2c91de..81ea4963b 100644
--- a/doc/pub/week44/ipynb/week44.ipynb
+++ b/doc/pub/week44/ipynb/week44.ipynb
@@ -2,37 +2,68 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "78b87f5a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
+    "doconce format html week44.do.txt --no_mako -->\n",
+    "<!-- dom:TITLE: Week 44: From Decision Trees to Bagging methods -->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2f70953",
+   "metadata": {
+    "editable": true
+   },
    "source": [
-    "<!-- dom:TITLE: Week 44: From Decision Trees to Bagging methods -->\n",
     "# Week 44: From Decision Trees to Bagging methods\n",
-    "<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University -->\n",
-    "<!-- Author: -->  \n",
     "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
     "\n",
-    "Date: **Nov 5, 2020**\n",
-    "\n",
-    "Copyright 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
-    "\n",
-    "\n",
-    "\n",
+    "Date: **Nov 1, 2021**\n",
     "\n",
+    "Copyright 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a02c8ce",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Overview of week 44\n",
     "\n",
-    "* [Thursday: Wrapping up PCA from last week and basics of decision trees, classification and regression algorithms with video of lecture](https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober29.mp4?vrtx=view-as-webpage) \n",
-    "\n",
-    "* [Friday: Decision trees, voting models and bagging with video of lecture](https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober30.mp4?vrtx=view-as-webpage) \n",
-    "\n",
-    "Geron's chapter 6 covers decision trees while ensemble models, voting and bagging are discussed in chapter 7. See also lecture from [STK-IN4300, lecture 7](https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf). Chapter 9.2 of Hastie et al contains also a good discussion.\n",
+    "* Thursday: Wrapping up PCA from last week and basics of decision trees, classification and regression algorithms\n",
     "\n",
+    "* Friday: Decision trees, voting models and bagging\n",
     "\n",
+    "Geron's chapter 6 covers decision trees while ensemble models, voting and bagging are discussed in chapter 7. See also lecture from [STK-IN4300, lecture 7](https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf). Chapter 9.2 of Hastie et al contains also a good discussion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4647cb2",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Thursday\n",
     "\n",
-    "Decision trees and wrapping up PCA.\n",
-    "\n",
+    "Decision trees and wrapping up PCA."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f524fd3",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Decision trees, overarching aims\n",
     "\n",
-    "\n",
     "We start here with the most basic algorithm, the so-called decision\n",
     "tree. With this basic algorithm we can in turn build more complex\n",
     "networks, spanning from homogeneous and heterogenous forests (bagging,\n",
@@ -43,7 +74,6 @@
     "Decision trees are supervised learning algorithms used for both,\n",
     "classification and regression tasks.\n",
     "\n",
-    "\n",
     "The main idea of decision trees\n",
     "is to find those descriptive features which contain the most\n",
     "**information** regarding the target feature and then split the dataset\n",
@@ -53,8 +83,16 @@
     "The descriptive features which reproduce best the target/output features are normally  said\n",
     "to be the most informative ones. The process of finding the **most\n",
     "informative** feature is done until we accomplish a stopping criteria\n",
-    "where we then finally end up in so called **leaf nodes**. \n",
-    "\n",
+    "where we then finally end up in so called **leaf nodes**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f2f4bf2",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Basics of a tree\n",
     "\n",
     "A decision tree is typically divided into a **root node**, the **interior nodes**,\n",
@@ -65,35 +103,58 @@
     "to our trained model. This is possible since the model has \n",
     "learned the underlying structure of the training data and hence can,\n",
     "given some assumptions, make predictions about the target feature value\n",
-    "(class) of unseen query instances.\n",
-    "\n",
+    "(class) of unseen query instances."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05895567",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## A Sketch of a Tree, Regression problem\n",
     "\n",
-    "\n",
-    "\n",
-    "<!-- FIGURE: [DataFiles/Regsimpletree.png, width=600 frac=0.8] -->\n",
-    "\n",
+    "<!-- FIGURE: [DataFiles/Regsimpletree.png, width=600 frac=0.8] -->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37cdf66f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## A Sketch of a Tree, Classification  problem\n",
     "\n",
-    "<!-- FIGURE: [DataFiles/Classimpletree.png, width=600 frac=0.8] -->\n",
-    "\n",
-    "\n",
-    "\n",
+    "<!-- FIGURE: [DataFiles/Classimpletree.png, width=600 frac=0.8] -->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2d9b41e",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## A typical Decision Tree with its pertinent Jargon, Classification Problem\n",
     "\n",
     "<!-- dom:FIGURE: [DataFiles/cancer.png, width=600 frac=0.8] -->\n",
     "<!-- begin figure -->\n",
     "\n",
-    "<p></p>\n",
-    "<img src=\"DataFiles/cancer.png\" width=600>\n",
-    "\n",
+    "<img src=\"DataFiles/cancer.png\" width=\"600\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
     "<!-- end figure -->\n",
     "\n",
-    "\n",
-    "This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using **Scikit-Learn**'s decision tree classifier. Here we have used the so-called **gini** index (see below) to split the various branches.\n",
-    "\n",
-    "\n",
-    "\n",
+    "This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using **Scikit-Learn**'s decision tree classifier. Here we have used the so-called **gini** index (see below) to split the various branches."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ad04098",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## General Features\n",
     "\n",
     "The overarching approach to decision trees is a top-down approach.\n",
@@ -107,12 +168,18 @@
     "* An instance is classified by starting at the root node of the tree, testing the attribute specified by this node, then moving down the tree branch corresponding to the value of the attribute in the given example.\n",
     "\n",
     "This process is then repeated for the subtree rooted at the new\n",
-    "node.\n",
-    "\n",
-    "\n",
+    "node."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c94e78e2",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## How do we set it up?\n",
     "\n",
-    "\n",
     "In simplified terms, the process of training a decision tree and\n",
     "predicting the target features of query instances is as follows:\n",
     "\n",
@@ -124,55 +191,28 @@
     "\n",
     "4. Show query instances to the tree and run down the tree until we arrive at leaf nodes\n",
     "\n",
-    "Then we are essentially done!\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
+    "Then we are essentially done!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c00ef40e",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Decision trees and Regression"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2nd degree coefficients:\n",
-      "zero power:  -0.6248402642829345\n",
-      "first power:  -0.06887270741953991\n",
-      "second power:  0.0003500424670389194\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 1,
+   "id": "f3a30cb2",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "\n",
@@ -268,7 +308,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "c009ba2b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Building a tree, regression\n",
     "\n",
@@ -287,7 +330,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "7c4c5803",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\sum_{j=1}^J\\sum_{i\\in R_j}(y_i-\\overline{y}_{R_j})^2,\n",
@@ -296,11 +342,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "81294adc",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $\\overline{y}_{R_j}$  is the mean response for the training observations \n",
-    "within box $j$. \n",
-    "\n",
+    "within box $j$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b81c1472",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## A top-down approach, recursive binary splitting\n",
     "\n",
     "Unfortunately, it is computationally infeasible to consider every\n",
@@ -313,8 +370,16 @@
     "further down on the tree. It is greedy because at each step of the\n",
     "tree-building process, the best split is made at that particular step,\n",
     "rather than looking ahead and picking a split that will lead to a\n",
-    "better tree in some future step.\n",
-    "\n",
+    "better tree in some future step."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44c7d005",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Making a tree\n",
     "\n",
     "In order to implement the recursive binary splitting we start by selecting\n",
@@ -323,7 +388,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "53b3b90c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\left\\{X\\vert x_j < s\\right\\},\n",
@@ -332,14 +400,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "9d26133d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "cbd3129b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\left\\{X\\vert x_j \\geq s\\right\\},\n",
@@ -348,14 +422,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "bea74001",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "so that we obtain the lowest MSE, that is"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "8ee87a04",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\sum_{i:x_i\\in R_j}(y_i-\\overline{y}_{R_1})^2+\\sum_{i:x_i\\in R_2}(y_i-\\overline{y}_{R_2})^2,\n",
@@ -364,7 +444,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "0d130f77",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "which we want to minimize by considering all predictors\n",
     "$x_1,x_2,\\dots,x_p$.  We consider also all possible values of $s$ for\n",
@@ -389,9 +472,16 @@
     "have three regions. Again, we look to split one of these three regions\n",
     "further, so as to minimize the MSE. The process continues until a\n",
     "stopping criterion is reached; for instance, we may continue until no\n",
-    "region contains more than five observations.\n",
-    "\n",
-    "<!-- !split  -->\n",
+    "region contains more than five observations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6b71a88",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Pruning the tree\n",
     "\n",
     "The above procedure is rather straightforward, but leads often to\n",
@@ -406,8 +496,16 @@
     "we consider a sequence of trees indexed by a nonnegative tuning\n",
     "parameter $\\alpha$.\n",
     "\n",
-    "Read more at the following [Scikit-Learn link on pruning](https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py).\n",
-    "\n",
+    "Read more at the following [Scikit-Learn link on pruning](https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef6ea8ae",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Cost complexity pruning\n",
     "\n",
     "For each value of $\\alpha$  there corresponds a subtree $T \\in T_0$ such that"
@@ -415,7 +513,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "d29beef3",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\sum_{m=1}^{\\overline{T}}\\sum_{i:x_i\\in R_m}(y_i-\\overline{y}_{R_m})^2+\\alpha\\overline{T},\n",
@@ -424,7 +525,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "59a93d07",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "is as small as possible. Here $\\overline{T}$ is \n",
     "the number of terminal nodes of the tree $T$ , $R_m$ is the\n",
@@ -439,20 +543,25 @@
     "having a tree with many terminal nodes. The above equation will\n",
     "tend to be minimized for a smaller subtree. \n",
     "\n",
-    "\n",
     "It turns out that as we increase $\\alpha$ from zero\n",
     "branches get pruned from the tree in a nested and predictable fashion,\n",
     "so obtaining the whole sequence of subtrees as a function of $\\alpha$ is\n",
     "easy. We can select a value of $\\alpha$ using a validation set or using\n",
     "cross-validation. We then return to the full data set and obtain the\n",
-    "subtree corresponding to $\\alpha$. \n",
-    "\n",
-    "\n",
+    "subtree corresponding to $\\alpha$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fc6fdf5",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Schematic Regression Procedure\n",
     "\n",
     "**Building a Regression Tree.**\n",
     "\n",
-    "\n",
     "1. Use recursive binary splitting to grow a large tree on the training data, stopping only when each terminal node has fewer than some minimum number of observations.\n",
     "\n",
     "2. Apply cost complexity pruning to the large tree in order to obtain a sequence of best subtrees, as a function of $\\alpha$.\n",
@@ -465,12 +574,16 @@
     "\n",
     "  * Finally  we average the results for each value of $\\alpha$, and pick $\\alpha$ to minimize the average error.\n",
     "\n",
-    "\n",
-    "4. Return the subtree from Step 2 that corresponds to the chosen value of $\\alpha$.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
+    "4. Return the subtree from Step 2 that corresponds to the chosen value of $\\alpha$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0735d893",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## A Classification Tree\n",
     "\n",
     "A classification tree is very similar to a regression tree, except\n",
@@ -484,8 +597,16 @@
     "classification tree, we are often interested not only in the class\n",
     "prediction corresponding to a particular terminal node region, but\n",
     "also in the class proportions among the training observations that\n",
-    "fall into that region.  \n",
-    "\n",
+    "fall into that region."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a00654f2",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Growing a classification tree\n",
     "\n",
     "The task of growing a\n",
@@ -503,9 +624,16 @@
     "When building a classification tree, either the Gini index or the\n",
     "entropy are typically used to evaluate the quality of a particular\n",
     "split, since these two approaches are more sensitive to node purity\n",
-    "than is the classification error rate. \n",
-    "\n",
-    "\n",
+    "than is the classification error rate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0147787",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Classification tree, how to split nodes\n",
     "\n",
     "If our targets are the outcome of a classification process that takes\n",
@@ -520,7 +648,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "7d999376",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i=k).\n",
@@ -529,7 +660,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "edcbd91e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "We let $p_{mk}$ represent the majority class of observations in region\n",
     "$m$. The three most common ways of splitting a node are given by\n",
@@ -539,7 +673,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "62267677",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i\\ne k) = 1-p_{mk}.\n",
@@ -548,14 +685,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "d7cf583c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "* Gini index $g$"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "bc77d53e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "g = \\sum_{k=1}^K p_{mk}(1-p_{mk}).\n",
@@ -564,14 +707,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "bd5855bc",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "* Information entropy or just entropy $s$"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "e4b76d0a",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "s = -\\sum_{k=1}^K p_{mk}\\log{p_{mk}}.\n",
@@ -580,126 +729,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "ddfc9c9b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Visualizing the Tree, Classification"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
-      "0          17.99         10.38          122.80     1001.0          0.11840   \n",
-      "1          20.57         17.77          132.90     1326.0          0.08474   \n",
-      "2          19.69         21.25          130.00     1203.0          0.10960   \n",
-      "3          11.42         20.38           77.58      386.1          0.14250   \n",
-      "4          20.29         14.34          135.10     1297.0          0.10030   \n",
-      "..           ...           ...             ...        ...              ...   \n",
-      "564        21.56         22.39          142.00     1479.0          0.11100   \n",
-      "565        20.13         28.25          131.20     1261.0          0.09780   \n",
-      "566        16.60         28.08          108.30      858.1          0.08455   \n",
-      "567        20.60         29.33          140.10     1265.0          0.11780   \n",
-      "568         7.76         24.54           47.92      181.0          0.05263   \n",
-      "\n",
-      "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
-      "0             0.27760         0.30010              0.14710         0.2419   \n",
-      "1             0.07864         0.08690              0.07017         0.1812   \n",
-      "2             0.15990         0.19740              0.12790         0.2069   \n",
-      "3             0.28390         0.24140              0.10520         0.2597   \n",
-      "4             0.13280         0.19800              0.10430         0.1809   \n",
-      "..                ...             ...                  ...            ...   \n",
-      "564           0.11590         0.24390              0.13890         0.1726   \n",
-      "565           0.10340         0.14400              0.09791         0.1752   \n",
-      "566           0.10230         0.09251              0.05302         0.1590   \n",
-      "567           0.27700         0.35140              0.15200         0.2397   \n",
-      "568           0.04362         0.00000              0.00000         0.1587   \n",
-      "\n",
-      "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
-      "0                   0.07871  ...        25.380          17.33   \n",
-      "1                   0.05667  ...        24.990          23.41   \n",
-      "2                   0.05999  ...        23.570          25.53   \n",
-      "3                   0.09744  ...        14.910          26.50   \n",
-      "4                   0.05883  ...        22.540          16.67   \n",
-      "..                      ...  ...           ...            ...   \n",
-      "564                 0.05623  ...        25.450          26.40   \n",
-      "565                 0.05533  ...        23.690          38.25   \n",
-      "566                 0.05648  ...        18.980          34.12   \n",
-      "567                 0.07016  ...        25.740          39.42   \n",
-      "568                 0.05884  ...         9.456          30.37   \n",
-      "\n",
-      "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
-      "0             184.60      2019.0           0.16220            0.66560   \n",
-      "1             158.80      1956.0           0.12380            0.18660   \n",
-      "2             152.50      1709.0           0.14440            0.42450   \n",
-      "3              98.87       567.7           0.20980            0.86630   \n",
-      "4             152.20      1575.0           0.13740            0.20500   \n",
-      "..               ...         ...               ...                ...   \n",
-      "564           166.10      2027.0           0.14100            0.21130   \n",
-      "565           155.00      1731.0           0.11660            0.19220   \n",
-      "566           126.70      1124.0           0.11390            0.30940   \n",
-      "567           184.60      1821.0           0.16500            0.86810   \n",
-      "568            59.16       268.6           0.08996            0.06444   \n",
-      "\n",
-      "     worst concavity  worst concave points  worst symmetry  \\\n",
-      "0             0.7119                0.2654          0.4601   \n",
-      "1             0.2416                0.1860          0.2750   \n",
-      "2             0.4504                0.2430          0.3613   \n",
-      "3             0.6869                0.2575          0.6638   \n",
-      "4             0.4000                0.1625          0.2364   \n",
-      "..               ...                   ...             ...   \n",
-      "564           0.4107                0.2216          0.2060   \n",
-      "565           0.3215                0.1628          0.2572   \n",
-      "566           0.3403                0.1418          0.2218   \n",
-      "567           0.9387                0.2650          0.4087   \n",
-      "568           0.0000                0.0000          0.2871   \n",
-      "\n",
-      "     worst fractal dimension  \n",
-      "0                    0.11890  \n",
-      "1                    0.08902  \n",
-      "2                    0.08758  \n",
-      "3                    0.17300  \n",
-      "4                    0.07678  \n",
-      "..                       ...  \n",
-      "564                  0.07115  \n",
-      "565                  0.06637  \n",
-      "566                  0.07820  \n",
-      "567                  0.12400  \n",
-      "568                  0.07039  \n",
-      "\n",
-      "[569 rows x 30 columns]\n",
-      "     malignant  benign\n",
-      "0            1       0\n",
-      "1            1       0\n",
-      "2            1       0\n",
-      "3            1       0\n",
-      "4            1       0\n",
-      "..         ...     ...\n",
-      "564          1       0\n",
-      "565          1       0\n",
-      "566          1       0\n",
-      "567          1       0\n",
-      "568          0       1\n",
-      "\n",
-      "[569 rows x 2 columns]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "32512"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 2,
+   "id": "c30ba6f9",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import os\n",
     "from sklearn.datasets import load_breast_cancer\n",
@@ -738,27 +784,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "1163bf61",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Visualizing the Tree, The Moons"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "32512"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 3,
+   "id": "7ddc76a8",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# Common imports\n",
     "import numpy as np\n",
@@ -788,7 +830,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "d8a311e5",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Other ways of visualizing the trees\n",
     "\n",
@@ -797,48 +842,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Text(167.4, 199.32, 'X[2] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]'),\n",
-       " Text(141.64615384615385, 163.07999999999998, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]'),\n",
-       " Text(193.15384615384616, 163.07999999999998, 'X[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]'),\n",
-       " Text(103.01538461538462, 126.83999999999999, 'X[2] <= 4.95\\ngini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]'),\n",
-       " Text(51.50769230769231, 90.6, 'X[3] <= 1.65\\ngini = 0.041\\nsamples = 48\\nvalue = [0, 47, 1]'),\n",
-       " Text(25.753846153846155, 54.359999999999985, 'gini = 0.0\\nsamples = 47\\nvalue = [0, 47, 0]'),\n",
-       " Text(77.26153846153846, 54.359999999999985, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
-       " Text(154.52307692307693, 90.6, 'X[3] <= 1.55\\ngini = 0.444\\nsamples = 6\\nvalue = [0, 2, 4]'),\n",
-       " Text(128.76923076923077, 54.359999999999985, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
-       " Text(180.27692307692308, 54.359999999999985, 'X[2] <= 5.45\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 2, 1]'),\n",
-       " Text(154.52307692307693, 18.119999999999976, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
-       " Text(206.03076923076924, 18.119999999999976, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
-       " Text(283.2923076923077, 126.83999999999999, 'X[2] <= 4.85\\ngini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]'),\n",
-       " Text(257.53846153846155, 90.6, 'X[1] <= 3.1\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
-       " Text(231.7846153846154, 54.359999999999985, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]'),\n",
-       " Text(283.2923076923077, 54.359999999999985, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
-       " Text(309.04615384615386, 90.6, 'gini = 0.0\\nsamples = 43\\nvalue = [0, 0, 43]')]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 4,
+   "id": "1ff7117d",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "from sklearn import tree\n",
@@ -851,7 +861,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "92a4adc1",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Printing out as text\n",
     "\n",
@@ -861,24 +874,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "|--- petal width (cm) <= 0.80\n",
-      "|   |--- class: 0\n",
-      "|--- petal width (cm) >  0.80\n",
-      "|   |--- petal width (cm) <= 1.75\n",
-      "|   |   |--- class: 1\n",
-      "|   |--- petal width (cm) >  1.75\n",
-      "|   |   |--- class: 2\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": 5,
+   "id": "038be2b2",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
@@ -892,7 +894,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "b4c8c97f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Algorithms for Setting up Decision Trees\n",
     "\n",
@@ -904,8 +909,16 @@
     "We discuss both algorithms with applications here. The popular library\n",
     "**Scikit-Learn** uses the CART algorithm. For classification problems\n",
     "you can use either the **gini** index or the **entropy** to split a tree\n",
-    "in two branches.\n",
-    "\n",
+    "in two branches."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d592e29",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## The CART algorithm for Classification\n",
     "\n",
     "For classification, the CART algorithm splits the data set in two subsets using a single feature $k$ and a threshold $t_k$.\n",
@@ -918,7 +931,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "a8b42f75",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}G_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}G_{\\mathrm{right}},\n",
@@ -927,7 +943,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "3dfe123d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $G_{\\mathrm{left/right}}$ measures the impurity of the left/right subset  and $m_{\\mathrm{left/right}}$\n",
     " is the number of instances in the left/right subset\n",
@@ -936,8 +955,16 @@
     "and so on, recursively. It stops recursing once it reaches the maximum depth (defined by the\n",
     "$max\\_depth$ hyperparameter), or if it cannot find a split that will reduce impurity. A few other\n",
     "hyperparameters control additional stopping conditions such as the $min\\_samples\\_split$,\n",
-    "$min\\_samples\\_leaf$, $min\\_weight\\_fraction\\_leaf$, and $max\\_leaf\\_nodes$.\n",
-    "\n",
+    "$min\\_samples\\_leaf$, $min\\_weight\\_fraction\\_leaf$, and $max\\_leaf\\_nodes$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d8afa0c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## The CART algorithm for Regression\n",
     "\n",
     "The CART algorithm for regression works is similar to the one for classification except that instead of trying to split the\n",
@@ -946,7 +973,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "9c03ca51",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}\\mathrm{MSE}_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}\\mathrm{MSE}_{\\mathrm{right}}.\n",
@@ -955,14 +985,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "349149a9",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Here the MSE for a specific node is defined as"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "5f132cdf",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\mathrm{MSE}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}(\\overline{y}_{\\mathrm{node}}-y_i)^2,\n",
@@ -971,14 +1007,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "b206ed41",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "with"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "386080b6",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\overline{y}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}y_i,\n",
@@ -987,14 +1029,24 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "d959190e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "the mean value of all observations in a specific node.\n",
     "\n",
     "Without any regularization, the regression task for decision trees, \n",
-    "just like for classification tasks, is  prone to overfitting.\n",
-    "\n",
-    "\n",
+    "just like for classification tasks, is  prone to overfitting."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1f63600",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Computing the Gini index\n",
     "\n",
     "The example we will look at is a classical one in many Machine\n",
@@ -1009,7 +1061,7 @@
     "humidity and weak and strong for wind.\n",
     "\n",
     "The table here summarizes the various attributes and\n",
-    "<table border=\"1\">\n",
+    "<table class=\"dotable\" border=\"1\">\n",
     "<thead>\n",
     "<tr><th align=\"center\">Day</th> <th align=\"center\">Outlook </th> <th align=\"center\">Temperature</th> <th align=\"center\">Humidity</th> <th align=\"center\"> Wind </th> <th align=\"center\">Ride</th> </tr>\n",
     "</thead>\n",
@@ -1029,85 +1081,28 @@
     "<tr><td align=\"center\">   13     </td> <td align=\"center\">   Overcast    </td> <td align=\"center\">   Hot            </td> <td align=\"center\">   Normal      </td> <td align=\"center\">   Weak      </td> <td align=\"center\">   1       </td> </tr>\n",
     "<tr><td align=\"center\">   14     </td> <td align=\"center\">   Rain        </td> <td align=\"center\">   Mild           </td> <td align=\"center\">   High        </td> <td align=\"center\">   Strong    </td> <td align=\"center\">   0       </td> </tr>\n",
     "</tbody>\n",
-    "</table>\n",
-    "\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2cb3568",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Simple Python Code to read in Data and perform Classification"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  (0, 0)\t1.0\n",
-      "  (0, 7)\t1.0\n",
-      "  (0, 9)\t1.0\n",
-      "  (0, 13)\t1.0\n",
-      "  (1, 3)\t1.0\n",
-      "  (1, 5)\t1.0\n",
-      "  (1, 8)\t1.0\n",
-      "  (1, 12)\t1.0\n",
-      "  (2, 3)\t1.0\n",
-      "  (2, 5)\t1.0\n",
-      "  (2, 8)\t1.0\n",
-      "  (2, 11)\t1.0\n",
-      "  (3, 1)\t1.0\n",
-      "  (3, 5)\t1.0\n",
-      "  (3, 8)\t1.0\n",
-      "  (3, 12)\t1.0\n",
-      "  (4, 2)\t1.0\n",
-      "  (4, 6)\t1.0\n",
-      "  (4, 8)\t1.0\n",
-      "  (4, 12)\t1.0\n",
-      "  (5, 2)\t1.0\n",
-      "  (5, 4)\t1.0\n",
-      "  (5, 10)\t1.0\n",
-      "  (5, 12)\t1.0\n",
-      "  (6, 2)\t1.0\n",
-      "  :\t:\n",
-      "  (8, 12)\t1.0\n",
-      "  (9, 3)\t1.0\n",
-      "  (9, 4)\t1.0\n",
-      "  (9, 10)\t1.0\n",
-      "  (9, 12)\t1.0\n",
-      "  (10, 2)\t1.0\n",
-      "  (10, 6)\t1.0\n",
-      "  (10, 10)\t1.0\n",
-      "  (10, 12)\t1.0\n",
-      "  (11, 3)\t1.0\n",
-      "  (11, 6)\t1.0\n",
-      "  (11, 10)\t1.0\n",
-      "  (11, 11)\t1.0\n",
-      "  (12, 1)\t1.0\n",
-      "  (12, 6)\t1.0\n",
-      "  (12, 8)\t1.0\n",
-      "  (12, 11)\t1.0\n",
-      "  (13, 1)\t1.0\n",
-      "  (13, 5)\t1.0\n",
-      "  (13, 10)\t1.0\n",
-      "  (13, 12)\t1.0\n",
-      "  (14, 2)\t1.0\n",
-      "  (14, 6)\t1.0\n",
-      "  (14, 8)\t1.0\n",
-      "  (14, 11)\t1.0\n",
-      "Train set accuracy with Decision Tree: 0.73\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "32512"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 6,
+   "id": "795e2e1d",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# Common imports\n",
     "import numpy as np\n",
@@ -1180,7 +1175,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "5e93ef08",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Computing the Gini Factor\n",
     "\n",
@@ -1194,73 +1192,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "X1 < 0.000 Gini=0.408\n",
-      "X1 < 0.000 Gini=0.408\n",
-      "X1 < 1.000 Gini=0.394\n",
-      "X1 < 2.000 Gini=0.394\n",
-      "X1 < 2.000 Gini=0.394\n",
-      "X1 < 2.000 Gini=0.394\n",
-      "X1 < 1.000 Gini=0.394\n",
-      "X1 < 0.000 Gini=0.408\n",
-      "X1 < 0.000 Gini=0.408\n",
-      "X1 < 2.000 Gini=0.394\n",
-      "X1 < 0.000 Gini=0.408\n",
-      "X1 < 1.000 Gini=0.394\n",
-      "X1 < 1.000 Gini=0.394\n",
-      "X1 < 2.000 Gini=0.394\n",
-      "X2 < 0.000 Gini=0.408\n",
-      "X2 < 0.000 Gini=0.408\n",
-      "X2 < 0.000 Gini=0.408\n",
-      "X2 < 1.000 Gini=0.407\n",
-      "X2 < 2.000 Gini=0.407\n",
-      "X2 < 2.000 Gini=0.407\n",
-      "X2 < 2.000 Gini=0.407\n",
-      "X2 < 1.000 Gini=0.407\n",
-      "X2 < 2.000 Gini=0.407\n",
-      "X2 < 1.000 Gini=0.407\n",
-      "X2 < 1.000 Gini=0.407\n",
-      "X2 < 1.000 Gini=0.407\n",
-      "X2 < 0.000 Gini=0.408\n",
-      "X2 < 1.000 Gini=0.407\n",
-      "X3 < 0.000 Gini=0.408\n",
-      "X3 < 0.000 Gini=0.408\n",
-      "X3 < 0.000 Gini=0.408\n",
-      "X3 < 0.000 Gini=0.408\n",
-      "X3 < 1.000 Gini=0.367\n",
-      "X3 < 1.000 Gini=0.367\n",
-      "X3 < 1.000 Gini=0.367\n",
-      "X3 < 0.000 Gini=0.408\n",
-      "X3 < 1.000 Gini=0.367\n",
-      "X3 < 1.000 Gini=0.367\n",
-      "X3 < 1.000 Gini=0.367\n",
-      "X3 < 0.000 Gini=0.408\n",
-      "X3 < 1.000 Gini=0.367\n",
-      "X3 < 0.000 Gini=0.408\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 1.000 Gini=0.405\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 1.000 Gini=0.405\n",
-      "X4 < 1.000 Gini=0.405\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 1.000 Gini=0.405\n",
-      "X4 < 1.000 Gini=0.405\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 1.000 Gini=0.405\n",
-      "Split: [X3 < 1.000]\n"
-     ]
-    }
-   ],
+   "execution_count": 7,
+   "id": "0ad7784a",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# Split a dataset based on an attribute and an attribute value\n",
     "def test_split(index, value, dataset):\n",
@@ -1326,7 +1264,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "e879e4ae",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Entropy and the ID3 algorithm\n",
     "\n",
@@ -1357,46 +1298,28 @@
     "training examples according to their target classification.\n",
     "\n",
     "The ID3 algorithm uses this information gain measure to select among the candidate\n",
-    "attributes at each step while growing the tree.\n",
-    "\n",
-    "\n",
+    "attributes at each step while growing the tree."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48065e96",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Cancer Data again now with Decision Trees and other Methods"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(426, 30)\n",
-      "(143, 30)\n",
-      "Test set accuracy with Logistic Regression: 0.95\n",
-      "Test set accuracy with SVM: 0.63\n",
-      "Test set accuracy with Decision Trees: 0.90\n",
-      "Test set accuracy Logistic Regression with scaled data: 0.96\n",
-      "Test set accuracy SVM with scaled data: 0.96\n",
-      "Test set accuracy with Decision Trees and scaled data: 0.89\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/MortenImac/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
-      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
-      "\n",
-      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
-      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
-      "Please also refer to the documentation for alternative solver options:\n",
-      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
-      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
-     ]
-    }
-   ],
+   "execution_count": 8,
+   "id": "7b948317",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -1443,29 +1366,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "819ca11b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Another example, the moons again"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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DVERERERiQQ1TEREREYkFNUxFREREJBbUMBURERGRWFDDVERERERiQQ1TEREREYmFyFd+kvx0di6kvf1meno20tAwiba2q2lpOT/qskREYk3ZKZIMapgmSGfnQlavvoL+/j0A9PS8yOrV3kqDClgRkWDKTpHk0KX8BGlvv/lAsA7o799De/vNEVUkIhJ/yk6R5FDDNEF6ejbmtV1ERJSdIkmihmmCNDRMymu7iIgoO0WSRA3TBGlru5qamsZDttXUNNLWdnVEFYWrs3MhixefwqJFrSxefAqdnQujLklEEkDZqeyU5NDgpwQZ6KRfjSNLNXhBRAql7FR2SnKoYZowLS3nV2WYZBu8UI2fh4jkR9l5kLJT4kwNUxkkjvP9afCCiMSdslOkeOpjKocYuOzT0/Mi4A5c9om6T5IGL4hInCk7RUpDDVM5RFzn+4tq8MLAoIHHH5/GiScu4Oijnyvr+4lIMik7D6UBV1IoXcqXQ8T1sk8UgxfSBw0MH97NmWf+gf3PncymfceU7X1FJHmUnQdpwJUUQw1TOURDwyT/UtTg7RBtH6qwBy8EnQGpr+9l7oxnuOdZNUxF5KBs2Rl139M4ZKcGXEmudClfDpHtsk9c+1CVS6YzHc3DXwm5EhGJu0zZOWbMWVWVmxDfs8eSDGqYyiFaWs5n+vSv0dAwGTAaGiYzffrXaGk5P7Z9qMol0+CAXXubQq5EROIuU3Zu3/5gVeUmaMCVFEeX8mWQTJd9sh0FNzYGPpRobW1XH9JPCmD//joeXfWaCKsSkbgKys6VKy8L3LeSzx4GZWc1rbQlxdEZU8lZtR0Fp58B2bt3BA89dA6rXjoy6tJEJCGqLTch+5U3kaHojKnkLNtRcGdnhIWV0cAZkL179/Ktb93ImjXj4Mh1UZclIglRrWcPq3WlLSmezphKznQULCKSH+WmSH50xlTyoqNgEZH8KDdFcqczpiIiIiISC2qYioiIiEgsqGEqIiIiIrGgPqYVIuol70REkkjZKRIvaphWgIGlQgemIxlY8g5IVMDqD4SIhEnZKRI/kV/KN7PLzGyJmfWY2Z1D7PsZM9tkZjvM7A4zawipzFirhKVCB/5AVNN60iLFUHYWT9kpEj+RN0yBDuAm4I5sO5nZOcBVwFlAGzAN+HK5i0uCbEuFJkW+fyA6OxeyePEpLFrUyuLFpyiEpRopO4uk7FR2SvxE3jB1zi10zv0a2DbErh8CbnfOLXfObQduBC4ud31JUAlL3uXzB0JnCESUnaWg7FR2SvxE3jDNw/HA0pT7S4EWMxsXtLOZXeJf5lqyZctQuZ1sbW1XU1PTeMi2pC15l88fiFzPEOjMgAig7MxI2Tk4O5WbErUkNUxHAjtS7g/8vzloZ+fcbc652c652RMmBOZvxaiEJe/y+QORyxkCnRkQOUDZmYGy89Dtyk2JgySNyt8NjEq5P/D/XRHUUlKlGFGZ9CXvBmrP5XNoaJjkB+fg7QOynRlI8uckUgBlZxbKzoPZqdyUOEhSw3Q5MAv4pX9/FtDpnEv0taZKma6kFHL9A9HWdvUhnxkMPkNQCYMaREpE2VnhSpWdyk2Jg8gbpmZW59dRC9Sa2XCg1znXm7brz4A7zexu4CXgGuDOMGstxFBH9HE7Qi30DMRZZ72erq63Ddre2LiDefN+UNIaczlDkMtZVZEkq+TszCWHKiU7J04cxZYtTYO2t7T00dGxpaQ1DpWdyk2Jg8gbpngheV3K/YuAL5vZHcBzwHHOufXOud+b2S3An4FG4J6058VOLkf0cTpCLfQMRGfnQrq6Lg18bM+e0aUvlKHPEORyVlUk4SoyO3PNoUrJzi1bgrOzs7O29IWSPTuVmxIHkQ9+cs5d75yztNv1fqCOdM6tT9n3G865FufcKOfch51zPVHWPpRcRkDGabqSQiebjuNk1JUwqEEkm0rNzlxzSNlZespNiYM4nDGtWLkc0cfpCLXQMxBDPd7S8gVWrVrBjBlfLbi2QiR9UININco1h6ohOxctmszEiReFmp3KTYla5GdMK1kuR/RxOkIt9AzEUI+bOTZt+imrVl1VcG2g+fVEqkGuOVQN2Ql9yk6pOmqYllGu88u1tJzPnDlPcMYZHcyZ80RgsIYRLIVONp3rGYpNm35ecG2aX0+kOuSTQ8rOoSk7JWnUMC2jUh3RhxUsxdQ7Zkxnhu2bUu71FVxboX24RCRZSnkmNAnZOXasslMklfqYllkp+uuEOS1KvvUOBP/ChZ88sM05MAvau/BRpnEagSsi5VWqfo5JyM577vnk0DsrO6WK6IxpAsQ5WIKC38xrnKabOPGigt8nTiNwRSQZkpadmSg7pZqoYZoAcQ6WbAHf3284B84ZEyd+qKiRpYX24RKR6pXU7Dx4hrRW2SlVR5fyEyBoWhSAvr5uOjsXRjq1R6aVQnbtbeT2+y5iSl0N8+a1ceaZFxf1PvmsBx21UqzfLSLFS2J2NjRMZs6cJ0r2PspOSRo1TBNg4Bfz+eevoa9v+4Htvb3bI18bOij49/fV8te1x5f8vZIwv97IkctZvfqbWr9bJAaSlp3lOpOZhOwsdPUsqTy6lJ8QLS3nU1c3YtD2qEdXDoxGNZuAc7BzVzP/8/cTWdU5NbKaClWKaWXGjfuLRsCKxEjcszMO87AWo1TTcWn2ABmgM6YJEmZH/n379nHffb/gxRfbc9q/r+/ddHT00767DjtiM1YD1rCX7l2jef75Vdx66w0lr7EUhg1r4K1vvYCmpmVZj9a3bv01J564gNNO62bX3iae6jgp8PXq6nYGbo/DYAuRahVmdu7YsZ177vk5O3ZsH3pnAN5x4H9//euzwLMlr6kcmptH88Y3HkZHx5eznuXM9fJ8nAeqSbjUME2QzH2SStuRf9u2Lfz0p7ex9Jkaunsbcn5e/+gdWEsXw+obGDGyie1929le28uTKw7HXOD8UZGrxWhv/ynveMe9QOaj9fb2LzB8uPf4qMZXmNf2GLs674WGMw95Tm/vKOrrBzdO4zDYQqRahZWda9as5K677mLV2mb2udyzM4nqrYcpU26ksTHzdFz5XJ4P63sk8aeGaYLk0yep0E7kmzZt5Pbbv8uyv49h/5SNWF0v5NimNGDsuLFc/sFP0jyimR/91+2sWrkK17aBgNmjYqHXwZrN43FuW+Dcqz09GwMvMdXV9tHVfgvNxxzaMN22bT6trX+KxfrdIuIJIzuXLXuSBQv+ixXrR+OmbsBq45p6pdHTbwwf3h38mH+WM595ZMPscyvxpoZpguQ6urKYTuSrV69g505jf30fNcN6mdw6ibbJbTnVN6p5FG943RsYVj8MgEv/z8d5YtkTrHtxXT5fZmj29+7nqWefZt/oXXR3NzNy5K5B+3hH8cGXknp7OgZt2737eKZPP0cjS0ViJIzsfO65pezc2UB/czd1w4wZ06YzYeyEMnw10et6uYsVz69k1ysjGDVicON04CxnPpfnkzR7gJSXGqYJk8voylKsdmIAZpw08yTOmntWQbWaGafOOpVTZ51a0PPLraenh2UrnmGf9bNkyTxe//o/UVvbe+DxgaN1LygHX2Kqa2gNfN0kjIAVqTZhZOfAVRcz4+x5ZzP9qOkF1xtn615cx8q1q3n02ZM5++S/Ulc3ODch/8vzyk4BjcqvSOpEnr+1a1/NCy+cEThCNmiC6t6+Wsa2fT6aYkWkLJSd+Vm54Wgef3w+vb2jCZpZQJP7SyF0xrQCqRN5Ybq6pjNnzt2Dtre0nM/+/ft47rlraGg4OCr/o294N10vd0VQqYiUg7Izf+vXz6C19XQ++MFPDHpMl+elEGqYViB1Is/N69ra2Lr554dsu/JKaGnpo6NjyyHbx49/F3/72zMsWzaOvUeuY9S44WGWKiIhUHbm5ryTT6Vr64ID9//93+HDHw7OTl2el3ypYVqBdJSam62bg3/8OztrA7eLSGVTduama+uwwO3KTikFNUzLKMp1f3WUKiJJpewUqV5qmJaJ1v0tzMmtrWwJOOqe0NLHkx2Dp2cSkcqi7CyMslMqhUbll4nW/S1MULBm2y4ilUXZWRhlp1QKNUzLRNOOiIjkT9kpUt3UMC2TTNOLaNqR+Bh/eG/g9paWvpArEZEBys74Gzt+X+B2ZaeUgvqYlommHYm/x9rbue6b1/NKVz/NnUcwc2Y3V1xxfUleO8rBGyJJpuyMv/uefJxv/eQ77N8ygom9jZx22vjAeUwLoewUNUzLRNOOVC8N3hApnLKzeik7BdQwLStNO5K/CS19GUeWJkWx622LVDtlZ/6UnVIp1DCVWKmEaU00eMOj6WtEwlMJv1PKTuUmaPCTSMlp8IZH09eISD6UncpNUMNUpOTa2q6mpqbxkG0avCEikp2yU0CX8kVKToM3RETyp+wUUMNUpCxKMXijVNOmpL9Off189KsvInEU5+yEtxZVl+RGl/JFYmhg2pSenhcBd2DalM7OhUW/TlPTAiZMWFOWukVEolTO7IQ7aW1VdpZbLBqmZjbWzO41s24zW2dm78+w38Vm1mdmu1Nu80MuV6TsSrVeeNDrmO1n2rSniq5xKJmmqUnS9DVxp+wUOVR5s3Mfxx67pOgas1Fuxud63veAfUALcALwgJktdc4tD9j3MefcvFCr82lFCglLqaZNybR/Q0N33jXlq1qmNolY7LNTuSlhKnd2NjaWNzuVmzFomJrZCOACYKZzbjfwiJndD3wAuCrS4lJoRQoJU0PDJP/S0eDtpXidnp4RBddWiELn5tOcfpklITuVmxK2cmfnnj3xz86k52YcLuXPAPqcc6tSti0Fjs+w/4lmttXMVpnZtWYW2Lg2s0vMbImZLdmyZVvRRZbq8oBILko1bUrQ6zhXz9q1JxVdYz4KnZsvijn9Tm5tZWrtlEG3k1tby/aeBYp9dio3JWzlzc5hrFgxu+ga81FIBkY1F2qpsjPyM6bASGBH2rYdQHPAvouAmcA6vPBdAPQCg1LOOXcbcBvA7NmzXLFFakUKCVOppk0Jep2urvls2RKHX/14StAE17HPTuWmhK2c2bl371vp6Ogtec2VolTZGYe/TruBUWnbRgG70nd0zq1NufuMmd0A/F8CwrXUSnV5QOJp2rS/M2vWwyxadFts+sGVar3w9Ne5//4FwNNFv27cJP3yVQFin53Kzcp2zJQ1nDHzKZqadrN48R2xyE0oX3Y+9NAf8I7xKkvcsjMODdNVQJ2ZTXfOrfa3zQKCOu+nc4CVrbIUbW1XH9JXCrQiRaWY0drO3JlPUF/vHQln6gf3la98g927Rx+4/7PPA0xh+Mgf8JaPXR5myRIgQWc6SyX22ancrFzTJ6zjDTOWUF/njRbP1n+4tXUCnQG/hy0tfXR0bCl/sZJV3LIz8j6mzrluYCFwg5mNMLO5wHnAXen7mtm5Ztbi//9Y4FrgvjDqbGk5n+nTv0ZDw2TAaGiYzPTpX4vF0aEUZ+4xSw80SgcE9YNLbZSm2rv7sLLVJpJJErJTuVm5Tpv2zIFG6YBM/YeDGqXZtkt1i8MZU4BLgTuAzcA24BPOueVmNhV4DjjOObceOAu408xGAp3Az4GvhFVkqS4PSLw0N74SuL2n50UWLz6Fnp6NDBvWCgy+JCm5mdDSl/FSUTmeV0Vin53KzcrU3JApNzcGrJi0IdziKkghGZj03IxFw9Q51wW8K2D7erwO/gP3rwCuCLE0qQK79jQxqikoZO1A/7h9+zRYoxiF9lOKon9TkkJd2SlR2dXTxKjhg3OztvawQVOESeEKycCo+tSXKjtj0TAVicrR416grnY/zoEd0uPO8LrhSZSi6JRfoQOlREqqfesRvGbSmkNys6amETPo69uT+YkSiiRnZ059TM2s0cxeNLP1ZtaQ9tiP/aXu/rEkFYWos3MhixefwqJFrSxefErgWrq57CPJ1L31fl5/9GKaGvYfEq51dWNQozQe8umUH8el/Ko1O5Wbla1v1x959RHtgw7mDz/8ffT2vhxVWZIiydmZ0xlT59weM7sO+DFen6ZbAczsZuCjwCedc78oW5VlkMuKJFq1JHdxm24iFzs3fJ362sG/eLW1I6itHTHoEtSYMZvYvn3ioP2Hj1QQx0Ecf86qMTuVm/lJYna67Z96BUUAAB1qSURBVLcFZKdj+/YHA6cIy5SdLTHsHlON4vZzls+o/DvxpiG52sxGmtmn8Za9u8459/1yFFdOuaxIolVLche36SYGZFuJom/fS4HP6enZGLjqx69+NZkvfvHtvOvKf+CDt3yI9X0beHrbUs676hNhfCmSXHdSRdmp3MxPErOT3s2Bz8mUnb/+9TQ6Or5PX9+mQ26aKkqC5Nwwdc714YXpBODXwDeA7zjnbihTbWWVy4okpV61RJe3wpct9GuHHRH4WEPDpEHT3AwbNok1a+axZs1xZaxWKlG1ZadyszJkbTDXHR74WFB2aoowyVde85g6534DPIU39cgC4FOpj5tZg5n9yMzWmtluM1vtnx2InUwrj6Ruz2WfXA1c3vIucbgDl7cUstEZNeVz7O87NHxTJ/9uaTmfOXOe4IwzOjjhhEfYtu3oKMqUClBN2ancrHw25pKcs3POnCfUKJW85NUwNbP3ASf4d3c559JHiNQBm4A34y2N9168y1f/UGyhpRZ0uSF9RZJc9smVLm/Fz4jx7+ThNXPY+UoTzkFPz0gd2cdM3DrlF6qaslO5Wflqm9/Mn1fOZmf3CJyD3t7Rys6YSXJ25jxdlJm9GW9FkXuB/cBHzOxW59zfB/bxVyK5NuVpT5vZA8BcvLMEsTHwC5Q6CXD6Or+57JOrUl/ekuwyDShIt2bbUTyz+kiaO49g5sxuzj47+/f26KOf49S5f6Z5+CusW/wgww7/ZKlKlgBx65RfiGrLTuVmsuWanau3HMlzzx3HxN5GTjttPG98Y/bvb/qk+4X+TEhukpydOTVMzWwO3tJ3jwIXApOBC4CbCZjcOeV5dcA84JaiKy2DXFYkKdWqJUEjFQe2V4K4TUpejoED48atYfbsRw8sX9rbs5HeF2/gVeNPYPnWV5X8/cKWxNHBcVet2anczF01ZGelz9Sg7CytIRumZvZq4AFgFfAu51wPsMbMbgc+bmZznXOPZnj6t4EdwM9KVXBStbVdfcgvJhR+eSuOovzly/UIP1UhoT916pIDjdID3F7mHLmM5SuS3zCN6+jgpFJ2Fq/ScxOqIzuzdcmohIapsrO0svYx9ddb/iNeQJ7rnNuZ8vANwB4yHNGb2dfxjvjPdc7tK025yZVppCKgEadFyveXf33fhkF/DKZN+zuzZt2d9fvQ0NAd+HojM6wZHTaNXo4PZWdpKDfLqxTZOXXqKiZP/lbW70Xcu2QoO+Ml6xlTf73lKRkeewloCnrMzL6JN/r0jc65rcUWWSnSL29V+uWNpJjR2s7cmU8cOBua6fvQ0zOC4cMHN0539wT+GpRdap+t2trD6O/fjXP7Af0sRU3ZWTrKzfg6ZsoaTj35r9TVZc/OuHXJUHbGW16j8nNhZt8G3oQXrJo9NwuNOI2HuccsHXSJPuj7sH79bPbvTzuWs+EsXvfacpc4SPo0On192w8E6wD9LCWLsjM3ys34mDvzyQON0gFB34tSztRQLGVn/JW0YWpmRwKXA68CXvDn49ttZr8r5fsk3cBlg6AjSIjP5Y1KFNQ/qrkx+FJ8+vdh27ajeeihc9i5x5teqq5hEk2Tv8TzW9vKUWpWQX+cg+hnKRmUnbnp7Fyo3IxIYHY2BXdvSv9exGnSfWVn/OU8XVQunHPrACvla1aa9MtQQSppxGnU1vdtGHKfXXuaGNU0uHEa9H1Ys+Y4lveOYNS44dz0uZvoerkLWFaKUvOSa2jm87MUt9HB1UTZObSB7MxEuVlaOWXnKyMYNWJw4zToe1GqmRqKpeyMv5I2TGVoQx2tRXF5I7W/DYyltfV4nt98wpDPi4tiQ+HRlbN4U0ofU4j/yN9MfbZS5fs1aFoTibNs2RnlZeGB7Dz88NFMmXISG16YFnodhSo6O589mbNT+piCslOKp4ZpyLIdrTU0TA590uHBZ3C38ZrXPMqmHY2s7h8ZWh3FKDYUVnW00bjjMF73uodpaOhOxOTPQdPoQD11dSPp7X05EV+DSD6yZWcUl4XTs7Ou7mVOPnkR2/r6WbtvQqi1FKrY7Fy54WhG9w3j1FOfoK5uZyJyR9kZf2qYhizz6MTJzJnzROj1BJ2FqKvrY+7sv7L68TeHXs9QyjWR8dq1r6apaSpXXHF9EdWFp5Sr64gkQbbsjEtfxbq6XubNWsLaJ84NvZ6hlCs716+fQWvr6Xzwg58oprzQKDvjTw3TkMVtwuhMZyGaR+wq+DXLuQqGJjI+KC59tkTCkJjsbNpd0OuVe/UgZedBys54K/l0UZJdnEYnQuYO3ru6mwt+TQWgiJRaYrLzlcK6QCk3RTw6YxqBOB2tBZ2F6O2t5dElp0dYlYjIYPHPzjoeWTo7wqpEkk9nTEsgycuZpZ+FgHE888xcVr1wTNSliUiFq6Ts7O09jCefPIOV618VdWkiiaYzpkWqhOXxUs9CPPzwg3R0PBhxRRJn5e4LJ9Wh0rLzF7+4gw0b1gOau1KCKTtzozOmRQp7ebwkn2EohUzz62ki4/CoL5yUgrIzXMrO6Ck7c6MzpkXKNDKzHMuZhXWG4fvfv5VXXhkNwL03Htye61FdOVfBqMajytRJvAuZ2iT9+fX189GvvkSt0rLz0kv/hR07Dg58uufL3r9xyE1QduabnUHPhRHlLVgA/XUqWua59Uq/PF62MwylbJgONErT5XpUV40BWC7F/kENen5T0wImTDgNVowvX+EiQ6i07ExtlKZSbkajmOzM/NwPlLVm8ahhWqQw59YL8wyDHGrr1l9z4okLOO20bnbtbeKpjpNCed9i/6AGPd9sP9OmPQUPzylprUEy9alKpf5V1UnZWR2KveJTqGKyM9Nz4R7gvBJXGqyas1N9TIsU5tx6mc4klOMMgxzkBesXGD68GzMY1fgK89oeY1fnvWV/72L/oGbar6Ghu+Ca8pHL2SL1r6pOys7KN3Dm0Tsz7g6ceQyjf28x2Zl5n21FVJSfas5OnTEtgbDm1ovbyifVInDpwdo+utpvofmYM8v63sVe7sz0/J6ewvtKlbsvnFQPZWdlC6v7WZBisjPTc2FcUTUpO3OjhmmCaI3faGQ6eu7tKf8llGL/oAY937l61q4tvCtCJV46ksqm7IxGlF0oisnOTM/t67sA6C24JmVnbtQwTZgwzjA0Ne0IHABV+Ud1ji1bOgdtraubSG/vS4O3N7SWvaJi/6AGPb+raz5btuhXX6pLubNz9OjdgQOgKj83MwtzgFu6YrIz03NXrBgBLCpn2YIaphLg0ks/w/Obm7EjN/LOs9/OWXPPirqksqipqaGmpgbqe9hVs4+XXurj1lu/OWi/I454Fccfv5m6uoN/YHr7azm87fNQV0+NGQzv4eWtjhUrOvnSl75UhmrPTfn/0/6tsOf39/exZWsjbtw2MEfDsIZSFBg5TV4tUfr+97/NX/+6ng29fdRP3MGlH/g404+aHnVZkYq6C0UxByNBz12x4g+lKCt24padapimiWoEoYSvvr6eC95yPnff+wt6J3Xwwo5m3Cv1g/b7+/Zj2Nhdy7xZT9Dc1M3uniYaJ15Oc8u7AXjbG97Gvb+/j94pG1m9YzTs6g/ta/jDXV+nZ8/gs9sNjTs45wOfy/Asw5q3YY37GD9uHOe+4dwM+xUvU5+q9H1KQZNXR0vZKeni3IWitXUCnQHZ0NLSR0fHlggqOlQ1Z6capinKOQmzQjueTn7NybS2tPL9n/+QnbU7M+73/N5xrFn8FsaOG8dlH7iUsYeNPfDYGXPOYGrrVH5494/ZU5f5NcohqFE6sL225eWMzzMzZr36BC5694XU1w9ujJeKzlRWB2WnZBLWALd8BTVKs20PWzVnZywapmY2FrgdeDOwFbjaOfcfGfb9DHAl0Ig3qdgnnHM9paijXCMIK2FN6Ep2xOFHcO1lX2Rj50accxn3q62pZdLESdTVDf61aZvSxnWf/iIvbR7cF7WcUlfmSvepj1ye8bHhDcM54vAjMLMyVCVhUXaKSKWJRcMU+B6wD2gBTgAeMLOlzrnlqTuZ2TnAVcAbgQ7gXuDL/railWsEYZRTZkhuhg0bxlFTjirqNRqHNzJt6rQSVVS8ONUiZaPsFJGKEvkE+2Y2ArgAuNY5t9s59whwP8Frf30IuN05t9w5tx24Ebi4VLWUaxJmrToiIqWm7BSRShR5wxSYAfQ551albFsKHB+w7/H+Y6n7tZjZoFlvzewSM1tiZku2bMlttYa2tqupqWk8ZFspRhBq1RGR8ss0EKCCp+tRdopI0eKWnXG4lD8S2JG2bQfQnMO+A/9vJm2tMOfcbcBtALNnz8rccTBFuUYQRj1lhlQurSRyUBUOFlB2ihSopaUv46j8ahO37IxDw3Q3MCpt2yhgVw77Dvw/aN+ClGMEYZynzJBki1ugSKiUnSIFisOUUBIsDg3TVUCdmU13zq32t80Clgfsu9x/7Jcp+3U653K73hShuE6ZISKJpewUkYoTeR9T51w3sBC4wcxGmNlc4DzgroDdfwZ81MyOM7MxwDXAnaEVG5HOzoUsXnwKixa1snjxKXR2Loy6JBGJmLIzO+WmSDJF3jD1XYo3t95m4D/x5tdbbmZTzWy3mU0FcM79HrgF+DOwzr9dF1HNoRiYx89bb9gdmMdPISsiKDsDKTdFkisOl/JxznUB7wrYvh6v037qtm8A38jv9aGvL5kdmtvbv5JhHr+vMH78eSV/P+fCW04zV4Wu4xu39X9FSk3ZGSzs3ASyLs4RlUIysJDnOOcS+XOSr/7++H2PK1EsGqbl1tn5El/96jVRl1GQefM2ErQ4z969G8vyNTnneHnHCFzzTgyorQ1eni3MRl+h6/jGbf3fSqDGfnXZtKk8OVNuYecmQG9vP9t3HoZN2ApATc3gC5Jh//4UkoG5PKempsb7fIfvZUfnKNrb1yXy5yRfzjm2vzwSmr0xg7U1uf8tUXbmrioapt17jaeeHzn0jjE068RmRo0cPHB2V3dz2b4mN64La9rLmDFjOen4kwL3UaOvOun7Xl1e6alJZHZGkZsYuImbsGG9HHnkUUydNHXQLpXy+9Pa0spR045i1crV7GntYNm6w8FVx/LGbtw2rKmHcWPHccJxJ+T8vEr53oehKhqmDNsPU5N5RPJo+zG86dV/o7724GWS/X21PNp+TNm+phqM6TNmcMl7P8awYcPK8h4ikgAJzc4ochOg1mp449yzePtZbws8Y1opamtrufQfP8GfHv4Tv/vLH+mf8lLUJYWmBuOYY4/lYxd8RH8fy6QqGqZjDxvD+ecO6oaVGDv7/srovl9Ryzb6GMfOYe9l5kmnM7NM7zduzDhmzpiJBV0LE5GqcdiowxKbnWHnJkDbpDbaprSV8R3io6amhnPOPIfjph/HmvVroi4nNBPGTOC4Gcfp72MZVUXDdETTCOafNj/qMoowH/hC1EVIHtSfSCpB88jmBGfnfJSb5TeldQpTWqeU7PWUnVK51xqkYhS6jm+U6/9WYn+ik1tboy5BRPJQSAZGvW66slOq4oyplF6Ya7QXepSso+vSyvaHIaw/WiJJFmZuQmEZqNwsPWVnftQwlYIovCSVfh5EhqbfE0mnn4nB1DCV2FOfIxGR/Ck7JYnUMJXYi2ufo2yhLyISNWWnJJEGP4kUKFvoRz2AQEQkrpSdko3OmIqUQSVeJgt74IaIVB9lp6hhKiI5GfiDkX4ZbktnLVNrp4Tab01950QkKZSd+dGlfBHJSxz6rcWhBhGRfMQht+JQw1B0xlRir5yXQeJ89Bjn2kQk/pSdh4pDbTI0NUwl9soZJMUcPZa731ASjmxFJL6Unbltl3hRw1SkQDryFhHJn7JTslEfUxERERGJBZ0xFclgau0UoLT9kiqh71Mcpj6JQw0iEkzZGSwOuRWHGoaihqnIEErZL6kS+j6V449Avn90kvKHSKSaKTsPpezMjRqmUtUyHT3GQdyPbEt5BqMS/uiIVBNlZ+GUndmpYSpVLTUEBi4/xUXcj2wrMRBFJDfKzsIpO7PT4CcRERERiQU1TEVEREQkFtQwFQlRpj5Ocen7JCISR8rO6qE+piIhinvfp6jEfbCCiERL2RmsErNTDVMRXyX+gpdTKT8v/dERSS5lZ36UndmpYSriq8Rf8HLK5fOqhEmxRSQ7/S7nR9mZnRqmIhVmd/dutu/YTt/OP9LX9UPo3Qx1h1M79uPUjnpzqLVs6QyeRmZLZy0bOjaEWouISC52dd5LV/st9PZ0UNfQyti2z9Pc8u5Qa6jmKaXUMBWpIEufW8rP7/0P2sau4axjn6S+1r801NvJ3pdu4sE//47VnVNDrOj0jI98/bZbQ6xDRGRouzrvZcvqq3D9ewDo7dnIltVXAYTeOK1WapiKVID+/n5+/cf7eeixh+jvhdNPWn6wUeqrr+3j9Lbl/P3Z10RU5aF6t4yOugQRkUN0td9yoFE6wPXvoav9FjVMQ6KGqUgFWLdxHY888Qj9PXXUvzSR5qbuwP2am7o5fkx4s8Tdn+WxMOsol2xfn4gkT29PcP/NTNul9NQwFakA+/btwzmH2zmaiYftpa9vNHV1OwbtN3z4ZG666YbQ6rr55syPhVlHudx884+iLkFESqiuoZXeno2B2yUckZ6yMLOxZnavmXWb2Toze3+WfS82sz4z251ymx9iuSKJsWPH2dTUNB6yraamkba2q0OtoyXD9CeZtktulJ0i5TG27fNYWnZaTSNj2z4fah3VvKBA1GdMvwfsA1qAE4AHzGypc255hv0fc87NC606kYTas2cW06e/gfb2m+np2UhDwyTa2q6mpeX8UOvo6NgS6vtVEWWnSBkM9CONelR+pU8JlU1kDVMzGwFcAMx0zu0GHjGz+4EPAFdFVZdUtmqaG66l5fzQG6JSfspOiUI1ZWdzy7s10ClCUV7KnwH0OedWpWxbChyf5TknmtlWM1tlZteaWcaGtZldYmZLzGxJ15auUtUsCZdtbriptVOYWjuFk1vVl0hiTdkpoVN2SliivJQ/EkgfnbEDaM6w/yJgJrAOL4AXAL1A4PAK59xtwG0Ar539WleCeqVKxHUC42xnLP7j4ZURVCQRUXZKLCUxOyvtbG8lKNsZUzP7i5m5DLdHgN3AqLSnjQJ2Bb2ec26tc+4F51y/c+4Z4AbgPeWqX6rbwBmA1FvUZwOqeSWQaqLslCRTdkqxynbG1Dk3P9vjfj+pOjOb7pxb7W+eBWTqvD/oLQArvEKR/CjEJAzKTqk0yk7JR2R9TJ1z3cBC4AYzG2Fmc4HzgLuC9jezc82sxf//scC1wH1h1SsiEgfKThGpZFEvvXIp0AhsBv4T+MTAdCdmNtWfb29gYe+zgGVm1g38Fi+YvxJBzZJg1TAHnFQFZaeEStkpYYl0HlPnXBfwrgyPrcfr5D9w/wrgipBKkwqV2tF9au2UCCsRKZyyU8Km7JSwRH3GVERyVM0rgYiIFErZmSxRr/wkkhhRh1i2aU1WrgmxEBGRPMQ5OyV+1DCVqjWhpU9z24mI5EnZKeWkhqlUraQFaKZJoj1TgDfR0LiDf/rolWGWJSJVprKy06NGdXyoj6lIQuQyF2DPntEhVCIikhy5ZKfmWo0PNUxFREREJBbUMBURERGRWFDDVERERERiQQ1TEREREYkFNUxFEiKXuQAbGneEUImISHLkkp1Rz7UqB2m6KJGEyD7B/kp+ePdt7N90GDreFBE5SNNAJYv+gomIiIhILKhhKiIiIiKxoIapiIiIiMSCGqYiIiIiEgtqmIqIiIhILKhhKiIiIiKxoIapiIiIiMSCGqYiIiIiEgtqmIqIiIhILGjlJ6lYJ7e2sqWzdtD2CS19WglERCQDZadESWdMpWIFBWu27SIiouyUaKlhKiIiIiKxoIapiIiIiMSCGqYiIiIiEgtqmIqIiIhILKhhKhVrQktfXttFRETZKdHSdFFSsTStiYhI/pSdEiWdMRURERGRWFDDVERERERiQQ1TEREREYkFNUxFREREJBbUMBURERGRWFDDVERERERiIdKGqZldZmZLzKzHzO7MYf/PmNkmM9thZneYWUMIZYqIxIqyU0QqVdRnTDuAm4A7htrRzM4BrgLOAtqAacCXy1mciEhMKTtFpCJF2jB1zi10zv0a2JbD7h8CbnfOLXfObQduBC4uZ30iSdHQ0EBDfSN1roH6eqOmJupjTiknZaeIVKokrfx0PHBfyv2lQIuZjXPODQpnM7sEuMS/u3tq7dSVIdSYi/HA1qiLiCF9LsEK/lwuvPCLJS4lNuL0s3Jk1AXkQNlZ2fS5DKbPJFicPpeM2ZmkhulIYEfK/YH/NxNw1sA5dxtwWwh15cXMljjnZkddR9zocwmmz2UwfSZ5U3ZWMH0ug+kzCZaUz6Vs1/vM7C9m5jLcHingJXcDo1LuD/x/V/HViojEg7JTRKpZ2c6YOufml/gllwOzgF/692cBnUGXokREkkrZKSLVLOrpourMbDhQC9Sa2XAzy9RY/hnwUTM7zszGANcAd4ZUainF7hJZTOhzCabPZbCq/0yUnZJCn8tg+kyCJeJzMedcdG9udj1wXdrmLzvnrjezqcBzwHHOufX+/p8FrgQagXuAjzvnekIsWUQkcspOEalUkTZMRUREREQGaLJDEREREYkFNUxFREREJBbUMI1AvutcVzIzG2tm95pZt5mtM7P3R11T1PTzMZiZNZjZ7f7PyC4z+5uZnRt1XRIu/W54lJvB9PMxWBKzM0kT7FeSgXWuz8EbjFDNvgfsA1qAE4AHzGypc255tGVFSj8fg9UBG4AzgfXAW4FfmtlrnHPtURYmodLvhke5GUw/H4MlLjs1+ClCZnYTMNk5d3HUtUTBzEYA24GZzrlV/ra7gI3OuasiLS4Gqv3nYyhmtgxvJPo9Udci4arm3w3l5tCq+ecjF3HPTl3KlyjNAPoGwtW3FG9tb5GMzKwF7+en2s8QSfVRbkrBkpCdaphKlNLX8Ma/3xxBLZIQZlYP3A381Dm3Iup6REKm3JSCJCU71TAtsTKsc13J0tfwxr+vNbwlkJnVAHfh9a+7LOJypISUnTlTbkrekpSdGvxUYmVY57qSrQLqzGy6c261v20WMb7EINExMwNuxxvw8Vbn3P6IS5ISUnbmTLkpeUladuqMaQTyXOe6YjnnuoGFwA1mNsLM5gLn4R3VVS39fGT0A+DVwDucc3uiLkbCp98N5WY2+vnIKFHZqYZpNK4B9gBXARf5/78m0oqicynetB6bgf8EPqEpT/Tzkc7MjgT+GW9qnE1mttu/XRhxaRIu/W54lJvB9PORJonZqemiRERERCQWdMZURERERGJBDVMRERERiQU1TEVEREQkFtQwFREREZFYUMNURERERGJBDVMRERERiQU1TEVEREQkFtQwFREREZFYUMNURERERGJBDVOpOmbWaGYvmtl6M2tIe+zHZtZnZv8YVX0iInGk7JQwqGEqVcc5twe4DpiCt+Y0AGZ2M/BR4HLn3C8iKk9EJJaUnRIGc85FXYNI6MysFlgKHA5MAz4G3Apc55y7IcraRETiStkp5aaGqVQtM3s78N/Ag8Abge865/4l2qpEROJN2SnlpEv5UrWcc78BngLOAhYAn0rfx8w+aWaPm9leM/tLyCWKiMSOslPKqS7qAkSiYmbvA07w7+5ywZcPXgK+CpwCvC6s2kRE4krZKeWkhqlUJTN7M3AXcC+wH/iImd3qnPt76n7OuYX+/lPDr1JEJF6UnVJuupQvVcfM5gALgUeBC4FrgH7g5ijrEhGJM2WnhEENU6kqZvZq4AFgFfAu51yPc24NcDtwnpnNjbRAEZEYUnZKWNQwlarhX1L6I7ADONc5tzPl4RuAPcAtUdQmIhJXyk4Jk/qYStVwzq3Hmxg66LGXgKZwKxIRiT9lp4RJDVORLMysDu/3pA6oMbPhQL9zbl+0lYmIxJeyUwqlhqlIdtfgLcE3YA/wEDA/kmpERJJB2SkF0cpPIiIiIhILGvwkIiIiIrGghqmIiIiIxIIapiIiIiISC2qYioiIiEgsqGEqIiIiIrGghqmIiIiIxIIapiIiIiISC/8/llFWAs/2i1QAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 792x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 9,
+   "id": "3bba539f",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from __future__ import division, print_function, unicode_literals\n",
     "\n",
@@ -1535,29 +1452,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "fa650f30",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Playing around with regions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 792x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 10,
+   "id": "7ffab598",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "np.random.seed(6)\n",
     "Xs = np.random.rand(100, 2) - 0.5\n",
@@ -1583,15 +1494,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "b97447c4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Regression trees"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 11,
+   "id": "e365b004",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Quadratic training set + noise\n",
@@ -1604,20 +1522,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DecisionTreeRegressor(max_depth=2, random_state=42)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 12,
+   "id": "104761d2",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.tree import DecisionTreeRegressor\n",
     "\n",
@@ -1627,29 +1538,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "70a9f35b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Final regressor code"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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YMmH3+amuVp6vogNXX3018+bNY8OGDR2WvluzZg0jRoxg8uTJ3HfffTZZmL74U7FUVamnKolQXQ39fvSyEQzlYTWK25Lwu81eRXqjt706QTdT12Hdtw905LzMBg5I5DyjRsHKlepXTxFESUkJ8+bN47///S/jxgXn+7nhhhvIz8+nvLzcHuMU6glLgvjj2Zbv87IVGPBhM0ecYM25qqqqGDt2rDWVW4Db7FWkN0baq926mZZZAsygxrfR1KRSCzgBu6cvhjBy5EgA/vvf/wbtf/nll1m2bBkzZsygW7dudpgWF1VVVXi9XmpqalJ6JR+HNSPH4o9naySTSuDDGv0hAUbbkFNXCIqE2+xVJAenaqdZ7TUp2imlTMmtCKSMsbWG2WId02HLzJRy1SqZLACpfW3u4rPPPgv/RjzXPBmbCXTv3l2eeuqpba8bGxvl0KFD5RFHHCGbm5t11RF63UaNGiWzsrJkbm6uzM/PlwMGDJAXX3yxrKmpMcVmKaV8/PHH5aBBg4L2rVixQmZkZJh2jttvv10effTRMjMzM+gaJYtVq6ScNSu4665aJWWnTlJmZGh/I3VroEY6QOOs2oqKinRdv06dpFzOKVKC/OT+N2IeYzdu1U6FwknYqZ2pO8LauTMcfTT06we9e2tb//5B+0Tv3uzv3Z/NvY9mf+9+iDBlwh1H797t53nlFfVsURGWkSNHUlNTg9ZP4b777qO2tpa5c+eSkZERd7133nkndXV17Nq1ixUrVtCvXz9GjhzJCy+8YJbpljNo0CBmzJhhy4xq/+PsO+/U/vpHBMLNglWExx/P1n+Il4nA4UOsi2GdOHGiZXVbgdvsVaQ3Rtqr3dqZujGshx4KOobfO/m2cFRXRw4wLsvMhOZmKo87LkFD0xxpwqofTkgQF4aRI0fyr3/9izVr1tC9e3cqKioYN24cp556qmnn6NevHzNnzmTTpk1MmTKFcePGsW/fPu666y4WLVrErl27OP7443nwwQcZPHgwAKWlpRx99NF89dVXVFVV0a9fP+655x7OOussqqurufrqq2lsbCQ3NxeApUuXtp3v1FNP5b333kMIwRlnnMGjjz5KXl6eYbsvu+wyoH25wmQSKT2LE2bBuomSEmCIlzFfAs3tIQHRdBPa0+jofRQ5ZsyYhG1NJm6zV5EcjLb7ZGGkvdqunYkMzzp50/NYKxqxhrjxP17asSOh8xgFlz7WihgSYBbhnlPYzOuvvy4B+cQTT8jLL79cZmdny6+//tpQHeFCAioqKjqUe+211yQgP//8c3nhhRfKs88+W27evFk2NDTIu+66Sw4bNkw2Nja21ZGbmytfe+012dTUJJ966imZnZ0tv/32Wyll5JAAf9sD5ObNm+XgwYPlzJkz28qcffbZsmvXrhG3f/zjHx3snj59umUhAZGaRLS+racZoUIC2jn3XClByhdeaLt+sR4N2qVhbtVORWrgpvbnVO1M3RHWBNGdJLelJcmWKcJi9/TFMJxwwgl4PB4effRR3n77bW6++WYGDhxoybn69OkDwA8//MDTTz/Nd999R69evQCYPn06c+fO5d133+Wkk04CYNy4cYwePRqAiy66iIcffpiFCxdy22236Tpfr169GDduXNAkgsCRWLuJNugeLT2LA5uRs/F6EYD0jbBakVxcCIH2W+cO3GavIr0Jba9O1k7bHFYhRHfgUeB0YCswTUq5MEw5AVQAlwG5wAfAb6SUn1ppn+4h7tZWK81QuJi8vDwOO+ww3nzzTXr37s3tt99u2bnWr18PgMejhaUfeeSRQe83NTXx/ffft73u379/0Pv9+/dvqyMSGRkZtATcoHXp0sWxCdJjOU5udUwdp5teLxLa8rBa8WjQbc6f2+xVpDErViBPOAH69AGvF/bs4ei6Jr5tyGQPXeiybw/5P2uCHLQ1mbt0oWTPHkqamuCP7fvYs0fTgNDXBJRpbmYIDErEXDtHWP8KNAK9gKOBl4UQH4UR1F8A/w84CfgOmAn8HTjWSuN0J8lVI6yKKBx//PF88sknzJ49O65YT708++yzHHzwwQwdOhSAL7/8kgMOiJxheO3atR1e//znPwfanV6jnHXWWbz11lsR3583bx4XXXRRXHUbJR7HKVbspUNwlm56vSwEJvpGWK1ILr5w4UJXTWRym72KNKW6Gk47jYWtrQS21vZ5PVu1HQ2+DWDr1o71hO6LUiYfChIx2ZYsAUKILsB44E4pZb2U8m3gn8AlYYoPAN6WUn4jpWwBngIOS4adJSUwbVoM0VUOqyICTU1NVFVVUVxczK9//WtLzvH9998zffp0FixYwH333UevXr2YOHEi11xzDRs2bABg586dvPDCC9TX17cd9+KLL7J8+XJaWlp4+umnee+995gwYQIAvXv3ZsuWLezevduQLcuWLaO+vj7iFuisNjU1sX//fpqbm2ltbWX//v00NDREqd0YfsepoiL2HLzqapg8WXOwQme/OglH6mZmJkshaNKVLt00gJNCTfTgNnsVaUpVFbS24qbWaldaq6FAi5SyNmDfR8DhYco+AwwWQgwVQmQCvwZeSYKNQURMihshJEAlIFfcc889fPvttzzwwANoT2jNoaKigry8PPLz8zn55JP56quvWLVqFePHjwdg/vz5DBs2jNLSUvLy8hgxYgTPP/98kA2XX345c+bMoWvXrsyYMYPFixe3xdeecsopjB49mgEDBlBQUMDKlStNs93PlVdeSadOnfjDH/7AihUr6NSpE8OGDYu7vnD9TY/j5I/XmjfPFSmtnKebvhHWQIc1kHh0MPSYhQs7RDw4GrfZq0hTSktBCBbSPpvWCDLKZhV2hQTkArtC9u0Cwj0z3QS8BawBWoDvgVPCVSqEKAPKAPr27WuWrWGDkI/NzNRiNMKMsDo0y5IiCWzfvp1XX32Vjz/+mD//+c/ceOONbatemUGVDk+qc+fOzJw5k5kzZ0Ys07NnT+bOnRv2Pa/Xy6JFizrsb25upqioqO11IkvLLliwgAULFsR9fCCJ9Dd/rKs/7FAIR6e0skQ3IQHt9HoZCyxp6piHNdL3cuyxkaMSwh0za9ZYlixZot8mmxk71l32KpJDtHZvCyUl7Cw6lUtq3uBhDuZHDmB47x10ogFycqCgAHbsAP+Tr5B9dXtga10OOymgKzvIpoEmcjhgSAG5jWGOa2hg9+ef70zEZLsc1nogP2RfPhBuBsd04DjgEGAzcDHwbyHE4VLKvYEFpZSVQCVAcXGxaY5+uAkcq/v0gW+/DTvCasVMWYU7ePXVV5k4cSIHHnggN9xwA3fffbfdJpmKHXlTY5FIfwuMdfV64bLLYNIkx/ZXS3QTEtBOr1fzcsOMsEb6XqK1oXDH2LG4RCK4zV5FcnCidm5s6E4ZMJV7WZTxKyp+qz2V0sOnATeXgdo5IIp2finE14nYa5fDWgt4hRBDpJRf+vYdBYSbwXoU8KyU0j+FeYEQYi5aPJbhhXnjmVgRdgLHY76VisKMsKoE5OnLhRdeyIUXXmi3GWlFIv3NiklCFmKbbkIE7fR6KYWwDms830u4Y444QseBDqJUCb7CJfTq0cIhwN89Ga7QTlscVinlHiHEYmCGEOIKtNmu5wInhin+HvALIcQzwI/ARUAm8JXR88b76DDsF+OfSR3GYXXZj6AizdATVuAmjPS3cE6XW1Jc2aWbEEU7MzMpBOrCOKzx6GC4Y/LyCh2bPi0chYXusleRvvTo1koesGCChxuudb522pnW6hrgMWALsA2YLKX8VAjRF/gMOExKuQ4t29eBwIdAFzTBHS+lNBwLkcijw9AvRtRq8x5khElXbvkRVCiM4J+45bRck6H9LZy4pkhsedJ1E6Jop9erxSOEiWGF8DoYqw2FHuM2589t9iqSgyO1s6VF67+/zIAAnXSqdtrmsEoptwPjwuxfhza5wP96P/Ab35YQljyqV2mtFIqkEy20J5y4ApSXa/MAWlv137A6LTerHboJUbTT66USKPvjH+Hee7V9/kTh9fXtoQKB+/xUV+u6qJWVla6KC3WbvYr0IkjTWlu1/ut7Yux07UyrpVkteVRvk8OqU+sVipQj1t1+6Gjgk0/CE0+0C67Ho++G1SmjCk4gonZ+/DE1QFk8o4qjRsHKlTEvak1NTcIOYDLTC5phr0JhBaGatuHoVq3/+hxWp2unXXlYbcPspNbJXJo1UHSdmtxcobCacI+nA/GPBmZkaH9BK+cX3NNO0yegsc6TboTVzu3btdQC8dDUpOuiVlbGfQag/ccz8LWVJGqvQmEVoZq2Y2uL1n8ztEnkTtfOtHNYTSeJI6yBX7obf0AdFbvjAtT1Ck+oqJaWBiebD13latKk9vLZ2drjLT03rOHOowjh4ou1LAHxkJmp66ImOuve/+MZ+NpKVJYAhVMJ1bTuBa2UAs8853GFdqZVSEA0IgUaxwof+N+HLYw4Ljk2Bn7pbvsBzcjIoKmpiSz/bZsiJs3NzXi9qouGEvp4GsI/fgrss/GEAqlsHzooK+PKFWv48Y1l5LfuIDsLyMmhPrOAph930NnT0LaPggL48UfwLRn8vwdXMkLHRU1kgQpo//Hct6/9tZUkaq9CYRWhmiamtFAOzHoig7efcYF2SilTcisqKpJ6WbVKyk6dpMzI0P6uWhV+X2B5fKuQjcr8T9B7VuM/bzLPaQYbN26UP/74o91muIpt27bJdevW2W1GEP725yRmzdL6KWh/Z82y9nxAjXSAxlm1GdXOnJwNurXzyT9tamtDoe9FYsOGDbrtiWZnstquGfYqUg8naufaAaVyA8hS/u0K7VTDN0SOt4iUAuvJJ+FmBjGYr/l7UwtPPpn80Re3jfZ0796ddevWAZCfn09mZmbQ2vaKdqSU7Nu3j61bt5q6xLAZzJs3z24TOqAW6rCPqirYv78Y2BhTO6urYeq0LOYBe+jMTQ36ZhsXFxezcePGhOxMpl6aYa8i9XCidnbLb2E4MCyOhQPsQDmstP/gNTRoa4n36AEjRkT/ETybgxnF1/yD5E26cjPZ2dn07duX7du3s3btWlpUOrCoZGdn06tXL3Jycuw2JQgnzn5Wj+7to7QUOnXaqEs7q6pgf2sWZcAe4HcZ+n4g3eb8uc1eRXJwonbm57ayEfj7FR5mXep87VQOK9qXNHcuXHutNipw/fXaD2CkH8FJk6Bhngck5HhbmDTJLsvdRXZ2NgcddBAHHXSQ3aYoUoxIC3U4LY9qqlFSAmeffS8vvTQ1pnaWlsLd2VmwH7Jo5MEH9X0n9957L1OnTrXwU5iL2+xVpDEtLdwLTP21p23hAD9O1E7lsPrYtk1L3RCYGDcwhUvolze372Zqv4O/3NPCYQYmaSkUbsafsseJowWh/U/lUU0Omzdv1K2dryzPpPInAM2UXdGKP1FNNO1024il2+xVJAdHamerNsL6xFMZDBXOW9mqA4kEwDp5MzJxQMrYk6xC38MfQP3KKzGPNxMcGLitSB+c2v7C9T+rJmOhJl0FEbd27t8f83gzcWrbVaQHTmx/dcOLpAR5gue/QX3Pqdqp8rD6CM0/Fm3lnKA8fr5YTLuTjJeXlyOEQAiBx+OhW7duHHfccdx+++1s3rzZknPW1tZSXl7Ozp3By5MvWLAAIQT1gcswmoSUklmzZnHIIYfQqVMnTj75ZD788EPTz6NwF+H6n8qjmhyuvbYoPu30JUeNpZ1FRUXWfgCTcZu9ivSlfncrRUBja0ZQ33OqdqqQgAAixcFFnYXsc1idMFO5a9euvPLKKwDs2rWL999/n4cffpjKykpeeeUV04W0traW3//+91x66aUUFBSYWnck7r77bioqKvjzn//M8OHDmTNnDqeddhqffPIJvXv3TooNCucRrv+pyVjJobKykqKiOLSzoQHy8mJqp9tWjnKbvYr0Ja9LK5VoS7MG9j2naqdyWHUQ7cv7v+daObinM75gr9fLyJEj216fccYZTJ48mZNPPplf/epXrFmzhgzfEmxuZP/+/dx9991MmzaNa6+9FoCSkhL69+/Pgw8+yMyZM222UGEXkfpfpJtQhXnk5eVFfC+aLj5wbyPF58TWzmj1OxG32atIX7pkt5AHTL42g8MnBPc9R2pnIvEETt6MxmSGx6EAACAASURBVGEZBV88ynjPIkvjriKdN5Tp06fLHj16hD1m2bJlEpDLli2TUkq5b98+efPNN8s+ffrIrKwseeSRR8qXX3456Jh+/frJqVOnyhkzZshevXrJLl26yIkTJ8qdO3dKKaVcsWJFmy3+rV+/flJKKR9//HEJyI8//liedtppsnPnznLYsGFy0aJFCX325cuXS0B+/vnnQfsvu+wyeeyxxyZUt0IfkdpfOoGKYQ1iyJAhhsr729BAz7e6tNNo/bHOazVm2atILRypnYcdJoeAlJ98kpTTJaqdKoY1UVpbbYlZNcLPfvYzvF4v77zzDgAXXHABCxYs4LbbbmPJkiUcd9xxnHPOOR1iQZ9++mneeOMN5s+fz5w5c3j55Ze54oorADj22GO55557AFi8eDHV1dW88MILQcdPnDiRc845hxdeeIEhQ4YwYcIE1q9f3/Z+a2srzc3NUbfAfK1ffPEFGRkZDBkyJOg8hx56KF988YV5F0yhUOimtrY2ruMyWht1aWe89duF2+xVpDEtLdSCFqzqApTDmiCZosVRQcnhyM7OpmfPnvzwww8sX76cl19+meeff57Jkydz+umn87e//Y0TTzyRP/zhD0HH7du3j5dffpmxY8dSVlbGX//6VxYtWsTnn39Ofn4+w4YNA+CYY45h5MiRHHPMMUHH33DDDUyZMoXTTz+dBQsW0NraytKlS9venzFjBpmZmVG3QYMGtZXfsWMHubm5HcIaunXrxt69e2n0TeJQKBTJo7y8PK7jOnkadWlnvPXbhdvsVaQxra2UA3jc4QqqGNY4kRMmwDPP8NIvWvjt9Q6M9QhBG42HN954g969e/OTn/yE5ubmtvdPPfVUFixYEHTM6NGjyc3NbXt9/vnnc/HFF/Pee+9x6KGHxjzn6aef3vZ/jx49OPDAA4NGWMvKyhgzZkzUOrKzs4Neh1vO1f/Z1FKv1uO/1gpFvMhjjoEPPuDxyQ0Mv8j52qlQmIEjtdP/BFM5rO5F1wIAvi/43LGtHVaIMP1cCbJ//362bdtGr1692LBhA5s3byYzM7NDudCRywMPPDDodadOncjNzWXTpk26zhuaOSArK4v9+/e3ve7du3eHc4QS6IR269aNuro6WlpagmzduXMnnTt3DvuZFAqFtQSOKOrSs6wsAC67qFGXdkYasXTqQi1qhFXhGvwjrC4JCVAOawi6V3jwf8EBMZb+4/WKaLJWk1ixYgXNzc2UlJTw73//m4MPPpgXX3wx5nFbtmwJer1v3z7q6+tNW1p1xowZ/P73v49apl+/fqxduxaA4cOH09LSwldffdUWjgBabOvw4cNNsUmhUBhj6NCh1NbW6tczn8NKQAhPNN301x+IY1fiIby9CoUjaWlhKFCrRljdSbgk1uGEsOhf/wJgdYDDalRE9Z4rEXbu3Mnvfvc7Bg8ezGmnnYYQgnvvvZfc3NyYTt7rr79OfX19W1jA4sWLEUJQXFwMaCOmQNCoqRGMhgSceOKJ5Ofn8/zzz3PHHXcAsHfvXpYsWeKs5e5SGH8u39WrV9tsicIp+OPSdWunb3Lnap/DGks3A+Pe/SRDO+MlnL0KhSO1s7WVpaBGWN2K3gUA3t+2TfuntbVtn1ERNXuxgebm5rZMAHV1daxevZqHH36YvXv38sorr5CRkcHo0aM544wzGD16NL/73e84/PDD2b17Nx9++CH79+9n9uzZbfV16tSJs88+m5tvvplNmzZx8803c95553HYYYcBtI1yzps3jwkTJtC5c2dGjBih297CwkIKCwt1l8/JyeHWW2+loqKCbt26tS0c0NraypQpU3TXo4if999/324TFA6jrq4OMKCdvvLRVroK1E1//YE4YaGWSISzV6FwpHa2tFAHKobVrRheACBghNWoiJq92MCuXbsoKSlBCEF+fj6DBw/m4osvZsqUKW2rQAkhWLx4MbNmzWLu3LmsW7eO7t27c/TRR3dw+iZMmEBeXh6XX3459fX1nHPOOTz88MNt7/fr14977rmH+++/nwceeIA+ffq0Pb63iltvvZXW1lZmz57Ntm3bKC4u5vXXX6dXr16WnlehUISnrKyM1atXG9czn8MaSzf99QfihIVaIhHOXoXCkbS2UgasdskIq3DkzDUTKC4uljU1NZbV758MJB96CCZPbttv9USAtvNa/L3179+fCy64oC3XqkIByWt/kUikf5nVN4UQq6WUxfHX4GySpp0HHADdukF9PY17GtnfmkVGt1y6NNe3x7cWFMDNN4MJIT92t11FemN3+wurfz17wrZtsGULHHCAsWPjIFHtVCOsiRIy6cq/nFl1Ncye7by7f4XCrYTGOs6dq2mtkyY4piNTp07l3nvv1Ve4urr9/x9/1DYgy7cR+jR961amXnUV94IpTmsyMHQ9FIokEEk7b25s5XfAvVFGWJ2kncphTZQQhxWc9QUrFKlCYKxjQwP85jcgpXMmOKYrRuLQ41kSsBBg0SLXOKyGrodCkQQiaefkllatf0WJYXWSdiqHNVECJl35cdIXHC9Wx6IqXM5hh8GOHZCToz223bFDU8LQ1xD/vpDXNzbChJYcdlJA19YdZLVqxzXuy6HT+QWQHbmu68nhzJYCurKDnJYGetwDVBq3gZwchkD78msKpk6dqr9wjMD+0IelApgKMH68MaNsxND1UCiSQGCcuMej+SatrZBBi9a/ojisTprgqBzWOLlyxAj43//CjrD26KF9//7RHyfNYFUoEuHKkSPhnXfg88+Tfu5soH+kNzdHP7YTcHTAa7Ed2B6fHflQELtU+lBYWMjGjRv1FS4p4cpzzoGPPoLOnSE7O+hmYGtTAXUbdpBFA/nUk089hdnZbHTJ6CoYvB6KtOHKK6+07dyBkxR79IApU6CpCTxSG2HdGCUkwEkTHG1zWIUQ3YFHgdOBrcA0KeXCCGUHAvcDo4AG4DEp5S3JsjUclWecEdZhra6G66/Xdns8WqyI20ZXFYpIVO7bZ+v5E1l8NxUW7nWibhqdoFX50kth9/tDqRo8mnb+Z8xsjn/xNmquuMIMM5OGlRPWFO6lsrIyKeeJNEEqcH6NfwHJDFqogZhprfzH2o2dybf+CjQCvYCLgIeFEIeHFhJCZAGvA/8GegN9gKeSaGd4/HckISEB/nCA1lZthNWfrlWhSAmOPz4pp5FhNiPvpzCO002zVnUK1c66Bm1hktqQFfecjlrlSmEX/pu+O+/U/gbOcfRTVQXNzVof89BKLaiFA6IhhOgCjAeOkFLWA28LIf4JXALcGlL8UmCjlHJOwL6Pk2JoFFb7RLQoZIQ1MN7D64V167RG44S7E4UiUVYPGABAUV4edOliWQxr3fYGttZp8aoF7KBnXgP5XdrL7N+8gx2bG2gFGskh7+ACDvAaiEXVa1eY17vXrt2ZlIsdglN1s7y8nCoDk6n8OUr9K//4CdXOrbs1h7X8P/9Bf+32Y/R6KNKDSO3eTPTMn/H3s4YGyGhtoRyocsnCAUgpk74BxwD7QvbdBCwJU/Yx4O/AMrRHYFXAiAj1lgE1QE3fvn2lleAb2JG//32H91atkvLqq6XMypIyI0PKTp20ffGwapWUs2a1H992XoXCBtra34QJlp5n1Sqt30TrP/PmSen1SunxJNbHjALUyBTSTWmXdoYhUDuvFo9ICXLzuWVxnUdpp8JJJKP96dFNKdu1U2oDrXLVf1ottctPotppl1udC+wK2bcLyAtTtg8wAS0WqxB4GXjJ98grCCllpZSyWEpZfECUJLimEmbSVUkJ9O2rvRV4p2MUPcP7CoUtCGsjQv2B/hUVkVNWffBB+2zXePuYy7BENyEx7SwzcUJUoHbul5qpv3m7ynA9dmqnmddDoTCCHt0ETTtbm7VwxjKgaqU7IvztmnRVD+SH7MunY9pogH3A21LKZQBCiHuAO4BDgY+sNFIXYRxWMCcVRLjhfYXCESThEVK0QP/qanjsMW14ALRHyGmQjcORullcbO6iX37tbN6fBRJOPLCr4Trs1E6zr4dCYYRYE6T82inQHNZjERxVmhzbEsWuEdZawCuEGBKw7yjg0zBlP8bJcyrC5GEF/Xc60fALd0aGSo+lcBg2xzxVVbXfKwoBl12WFnHijtRNs0cU/dp5/gRthPXGw/oarsNO7VQjrAon49fODDQBvcLjdY122jLCKqXcI4RYDMwQQlyBliLxXODEMMWfAqYKIU4DVgC/RYvJSn4iyHBEGGGFxFNBOCn/mUIRhMUhAbEIfYIxaZKt5iQFp+pmXl4edXXhBnnjp6QE2JIFT0Peiy+GHUKOdbxd2mnF9VAozMKvnZ6GVmiFbq1NhvuXXdi5cMA1aBMDtgDbgMlSyk+FEH2Bz4DDpJTrpJRrhBAXA48ABwLvA+dIKRvtMjyIKA6rGTgl/5lCEYTNI6xpfDPnON20LEl+ljbCujHO4VG7tFMtGqBwMn7t/M9rrVAOGzt1stsk3djmsEoptwPjwuxfhza5IHDfYmBxkkwzxuOPwz/+of2vI5XOPtpT9XQijvQ6flSuLIWdJHmENVwy7HS8mXOiblZVVTF27FjzK87O1urfsoWxRE6I7jQsux4KhUGiLiJweAuUQ5WUuKW1qqVZ46TmtNPgjTdgu/71HSWQg5bF2/867p/9UaNg5UpnK7ci5ai54w6YOTOpI6z+Gd/+x//xxoQrrKGystKQg6Z7JSjfCGvl+vX0dFEbMHo9FOlBsldAi6mbvvk3lc3NrnFYXZIt1nkUbdqE0fS/IswWN01NKm2AIukU9e2rtfskjrCqbBnOZsmSJYbKFxUV6Uue7nNYlwwe7Ko2YPR6KNID3e3eJGL2GV8445K8cFnxnIlyWOPl6qsNH2LqcpKZmSptgCL5+LNiJDDCWl0Ns2frz42psmU4m4kTJ1pTsc9hnfjll65qA5ZdjzioqqrC61UPUlMFI9oZs8/4tHzi3r1mm2kZqiXHSdnHH8NJJ1G5bZsWZwoxY1gFCcawSgnffae9r8IBFDZQ9ve/A1AZ5wir3sf7obFXaTrByhWMGTPGUHl/2qfKysroBX0O65jOnV3VBsJdj9LSUqqrq8nKysLj8dCjRw9+8pOfcP3115s26rZgwQJmzpzJV199ZUp9oWzZsoWbbrqJlStXsm3bNnr37s3ll1/OrbfeirA5a4gb0N3uI6BHOw3ppm+EdYwvVtwVJLJMlpO3oqIiXUuFxQsxlhcMXBLQNJ57Ti0vqLCVtvY3eXJcx8+apS0bCNrfWbM6ltG7vKBl/SwG2LQ0a7I2u7Szw/f55ZdaQxk0yNLzJoNRo0bJioqKttdr166Vt99+u/R6vXLx4sWmnOPxxx+Xg0Ku1YoVK2RGRoYp9X/99ddy9uzZ8ptvvpGtra3yf//7n+zXr5+cM2eOKfWnOom2v1jaaVQ333tpg1ZZ795x22SURLVThQSYjNElAQ09HrU5lZBC0UacbVHPo91osVf+/lJZqZYtdgpmjK6F1U3fCKv4+uuIxxgJLUkWeq5Hv379mDlzJpMmTWLKlClIKdm7dy833XQTAwYMoHv37px55plBo6WlpaVcf/31jBkzhtzcXA4//HCWLVsGQHV1NVdffTXffPMNubm55ObmUhXQcZ599lkGDRpE165d+eUvfxlXntiBAwdy6623MmDAAIQQHHHEEfziF78IOo/COmJpZ6yY1epqmDxZO+7OO+HCX2ojrGLzZuuNNwnlAZmMkckBhte7Vg6rwinE6aToWQEukjAH9pdrr9UiZdwwCSfV0QZOEiOsbvocVtmrV4fyhrUziRi5HhMmTGDDhg2sWbOGK664gi+++IJ33nmHzZs3c8IJJzBmzBiamprayj/66KNcd9117Ny5k9tuu43zzjuPtWvXUlJSwiOPPMLAgQOpr6+nvr6eUl/HaWlp4bXXXuOjjz6itraWDz74gPvvv7+tzjFjxlBQUBBxW7hwYVjbW1tbWbFiBUceeWR8F0phiFjaGc2h9feXefPa+1lLkxbDKg85JGmfIVGUB2QyRiYHGJ75quKEFE4hgZunkhKYNi1yHGIkYQ7sLy0tmglumIST6kRyaIwQVjd9DuvCMKOBTs4aYOR69OnTB4AffviBp59+moceeohevXqRlZXF9OnT2bRpE++++25b+XHjxjF69Gi8Xi8XXXQRxcXFus539913k5ubS69evRg3blxQiqWlS5eyc+fOiFukSWQ33ngjdXV13HTTTbo/ryIxomlnNIfW31/891JCQE6mNsK6cN8+6w03CTXpymSMTA4IXV4y9I6oQx1qhFXhFHTePMWb7D3cogCh/WXuXNi2zfmTcFKdpUuXJjwzPqxu1msO69K9e5nYv3/QhNQpmQVMaNlBFg00tuRwwOMFMG8HDXUNNDRBZpccOvUOmciaJIxcj/Xr1wPg8Wl76GhlU1MT33//fdvr/v37B73fv3//tjoikZGRwQEHHND2ukuXLgkvHXvjjTeybNkyli9fTteuXROqSxGeeLQz0mIqgdrp9cJll0HZKa3wS1i6bx/OyWsRHeWwWkCsFXgCG2I45zbibEDlsCqcgo62aHbCfzfNFE8nzBhhhTC6+f77Wv3Qlh3F/7C9i2/zI77U3svybdSB3Bwh13VlJfhmbFuBkevx7LPPcvDBBzN06FAAvvzyyyDnMpS1a9d2eP3zn/8caHd6jXLWWWfx1ltvRXx/3rx5XHTRRYAWBnDVVVdRXV3NypUr6d27d8TjFPGTFO2s1UICFhYWmmFyUlAOa5wce+yxgPG7oHANcdq04DLhHncph1XhBI49+GDYsCHmCGt1NZSXa4Nbra0h7TgB0nE5VqczduxYQ8nydWvn229r9QP+2qO1Ot0BU4sWWeqw6rke33//PX/7299YsGABzz77LL169WLixIlcc801zJ07l4MPPpidO3eyYsUKRo8eTW6uturuiy++yPLlyyktLeW5557jvffe48knnwSgd+/ebNmyhd27d5Ofn6/bXv/ErVg0NzdzySWX8MUXX1BVVUXPnj11n0PR3u5jkTTt9KW1GrtxI25Z6kI5rHGyevXquO6CIjqjAYQO369bpzXiEhXDqrCZ1dddB7fcEvXmyd8v/ILr8ag401SmzKDzp1s7f/Yz8Hopa24Gwi+0EqiIkaY6dVDN8eMN2WuUSNejoqKCP/7xjwgh6NGjByeeeCKrVq3i+OOPB2D+/PnMmjWL0tJSNm/eTEFBAT/96U85/fTT2+q4/PLLmTNnDueeey6HHHIIixcvZuDAgQCccsopjB49mgEDBtDS0sJLL71k6uf6z3/+wzPPPEN2dnZQaMJPf/pT3U5vOrP6wQfhySe1ZdW/+y5srvaGRui9PYfZFPAQO8imAU8LdLs/B57Tkas9Vj73wH1btgBQ1tLiczCcPxKgHNYE0ON8hhItbhXaRx3mzoUPPoDHHoP58+GJJ6DmD2qEVWEzOla68vcLv7N62mnaiIFePYw37lVhD6Vx3Ino0s6SEnjzTUr/8Af45BMa6xrYth32k8MuChje27f4iu8HeP/mHezf1UBrK+xq0MoUsIPC7g1k798N/hV9LBxdhfDXQ0/qp86dOzNz5kxmzpwZsUzPnj2ZO3du2Pe8Xi+LFi3qsL/Z5/D7KS8vj2lLOEaNGmVKRoi0pLpac1QDMj6EIwvo79sCEZsBHdmnAr8dvcNbpfv3a7a5YDEi5bAmQCznMxzR4vBCRx1+/ev2GdGNjfDR/5TDqrCZwGmmEQjtF0adVTNjtxTWU1hYaHgSj27tLCmhcOVK6urqyAa+C7iZ6RRBOz0eaPFoN0wZGVBxE0zbcwf84Q9xfT6jxHM9FClOVVVMZxUMhLWYeHwhUNfUZE7cgcXocliFEI8AVwEHSyk3hrw3DPgf8LCU8jrzTXQm/uTQq1ZJ02byhY46QLCoH3mMBx43w3qFIj6EL+Ba+kZYw42GJjI5Kp6nFgp7MeqcGdXOwPr1aKeUmtMqRIAzvCx5N/vKWVV0oLS0zZmUIX8hcmiLEQc0VshMJOoAMjNdEbOld4S1Gs1hPR54MeS9vwC7gXLzzHIPRieBRHvcGTrqMGmStvnLH75fjbAqHIIQUUdD450cFc9TC4W9VFZWGo5jBf1txF+/Ee3skPLstQzD9sVLvNcjFmpFKRcT2GAHDGDfvlZ2bG7oGN4CCKOxqL59+8nhi80FdGUHOTTQoztk58c4TggqCwooe+ghV4wM6HVY3/H9DXJYhRBnA2cBv5FS7jDZtpQj1uPOSCNTbWWq1KQrhTNYv9Fj2UxWlbrKXdTU1FjioAXWP2JEWVza2UZG8hxWq6+Hwt2sPfs3XFU7lTe2BISt/LZjtiCjdAL2V8MrVVofKNSpnTVlZZS5RGh1OaxSyjVCiO1oDisAQohMYA7wCTDPGvNSCz2PO0NHHYJGFVRaK4VD+PtTgjdarckCoFJXuYvKykrL6589O0HtTKLDavX1ULibh+Z5eKPFOdrppvZqxAN6BygWom22xXXAUOB6KWWL6ZalIEaWbYWO62V/8plyWBXOoKnFE5QFQE2OSl/iyRJgtP5EtfO79clzWK2+Hgp309TsLO10U3s1kiXgHeDnwDDfaOudwItSyuWWWJaCGH3cGToi+/6HymFVOANPhiBD6ssCoNJUpTbxpkkyUn+i2vnVt8lzWK2+Hgp3I7weMlqdo51uaq9GHNZq39/jgZOBbGCq6RalOEaG7EMnEhxTpBxWhTOYdJmHjAGxhVSlqUp9/MuKWl1/Ito5cGgGJCm3vdXXQ+Furv2thwN6OEc73dRejTis7wKtwOXAScCfpZTfWGKVC5g3z3jYrtG7pdBRhREeNelKYS/zzjoLli2jbz+PrkkCKk1V6lNcXMzGjRtjF/RhVDuLi4tZtGhjQto5oCZ5I6xGr4ciPZh38snw5psMHORh2uTY5ZOlnW5qr7odVillnRDiM7TR1c1AcrIwOxSjs0DjvVsKGlV4T42wKuyl7KijYNmyqAsHBKLSVKU+Rn/sjGrnokUbE9fOD5LnsLrlx1+RXMqGD4c334y6SmAgydJON7VXox7Qf31/p0kpVXZkA4S7WzKMyhKgsBv/Slc626J/pKuiQoUDpCr33nuvpfVXVNybuHYmMUuA1ddD4VJ0LGsdSLK0003tVfcIqy+NVSlQAzxhlUFuwZ8KQu9ogSl3S8phVdhM5QcfAFCmc4QVwsceqolYqYPRERqj2tm168bEtTOJDqubRqwUyaPys88AKDPwO54M7XRTexVShlvQK0xBIaahhQGcKKV8J1Z5uykuLpY1NTWW1e/P7qX3+oEJDe3DDxHHHGP4vAqFWbS1+z/9CW6+Wdcxoe3eSHiMEx1bIcRqKWWx3XZYRUpq5+OPI/7f/zN8XoXCLNra/WOPwWWX6TpGaWcwUUdYhRDdgTOAI4GbgTlmOau+uh8FTge2ooUZLIxxzL+BnwGZUspmM+xIJvEmRPc3vJ/3USOsCmewfIWg80mx23M4gdU7mUBlGOiIE3WzqKiI1atXm11th/oT0c4L9iVvhNXq66FwN0v/5aHHcOdop5vaa6yQgDOAhcAW4C/ArSae+69AI9ALOBp4WQjxkZTy03CFhRAXYSyrQUoQ2PAWeZXDqnAGy17x8FBVbCcyVGCffFLb7/X15GiPeFWGgbA4TjeTsdJVPARqZ61HrXSlcAbPL/Lw/MvO0U43tdeoHpCU8mkppZBS9pJS3mzWilZCiC7AeOBOKWW9lPJt4J/AJRHKdwWmA7eYcX43EdjwGpqUw6pwBs3So2sCTOAKRV4vPPYYzJ+vzd268sroom10daNUx6m6mZeXZ2X1cdcfpJ3NyXNYrb4eCnfjNO10U3u1ywMaCrRIKWsD9n0EHB6h/CzgYbR0WhERQpQJIWqEEDU//vijOZbaTGDDy8hUDqvCGQghdDmRgTNdL7tMcx78W9++0UcYVIaBDliim5CYdo4ZM8ZQeaPEW3+gdorM5DmsVl8PhcsRHkdpp5vaq+5JV6aeVIifAs9LKXsH7LsSuEhKWRpSthj4G1AM9AG+RUcslhMnDsSLPw7rzAFrOPbC4Uk7r0IRir/dvzrmfvJum2LIiUyVmFS7Jl0lQzchNbXzfBYz/LbxSTuvQhGKv92/MOEZev32V0o748CuIbt6ID9kXz4QlNtVCOEBHgKuc+MkK9NRaa0UDuH0M4RhwVQjpgnjSN20ei1yM+qXSYxhddPa7IrkM+58j6O0003t1a5JTLWAVwgxREr5pW/fUUDoxIF8tBGCZ313J37VWS+E+IWU8q2kWBuGZN2lB95ZPRVm0pUTU1coUhc5eTI8/HDUm6fq6vYJApMmBbfLeDNlKIAU0E2wRztrwuRhVdqpSCby/PNh8eKI2ulvjz16wLZtHdul0k6bHFYp5R4hxGJghhDiCrTZrucCJ4YU3QUUBrw+BG21rSIgNYJUYxA0cUAGN/RUeUygcA+bN7XSG/jmW8HAMO9XV8PPfgYNDdrrxx5Ts/vNwqm66dQR1qCZ0knUTjeNWCmSx7Ztkh7AmlrBsJD3/O2xoUFbEMvjgezs5Pymu6m92vmM+RqgE1rKrKeByVLKT4UQfYUQ9UKIvlJjs3+jXWx/kFI22mV4Mok26cqU5V4VCp1UV8PLS7TRsb/c56G6umMZf5v009Sk2qXJOE43hw4danaVptQfNOnKGzzCaqV2Wn09FO6juhpWvaUtzXpXeUft9LdH/+qtra3J+013U3u1La+plHI7MC7M/nVAboRj1gL614S0kKKiIgDTEu5Gejzlj12pqoLThwuKz29/z5TlXhUKnVRVwSMti3gIOK5ZhB059bdJ/whrZqZql2biRN1cunSpofJGtTNW/Xq089wuGSy5rv09K7XT6PVQpD5VVfBoaxXlwCHNng7aGaib/hHWZP2mu6m9pl0ifrN4//33Tasr1uOpttiV9cEjrIGCrOKwFFZTIc80NgAAIABJREFUWgq3sY11wAleT1gxLSmBFSsix7AqUo+6urrYhQIwqp3R6tetnSuCR1it1E6j10OR+mjauQuA/mG0M7A9RophtQo3tVflsDoA3atShAnWVoHYimQR2M5uvNnD4AjtTrXJ9KKsrMzSpR2j1a9bO8NMurKqnVp9PRTuI7CdzZzl4dAw7c4u3XRTe1V5khyA7lUpVForRZKprobZs+kQczV4sCMicxQOwOofu2j169bOMA6rVbjlx19hHZF0E+DQw531O+6m9uqsK5em6M6xJtqdhEidIZRoHUehiIb/ceudd2p/g9qQunlS+Jg6dapt9evWzgCH1WrdtPp6KJxNVN0Ex2mnm9qrCglwCLoeBwQ09DvvjJ2OJTS+a+5ca2JjVD7D1CTqTGqhRlgVGoWFhbELWVi/Hu38+NN2h/XUU43p5vLl2n69GmfkeijtTD1ihqk4zGG1uv+aiXJY3URAQ48Zs0VIDtcG+M1vQEpz8w6qXLCpS9SZ1DEWDlA/wumDnSOseqn5oN1hNaKbjY3aBMInntCvcXrtVdqZmsTMQOEw7VQjrGnAlVdemfyTBjT0SDFbgQ0+sON4PJoAB+Z3M6ND6J70oHAk0QQy3EzqKwcNgq+/jjjCqn6E04/CwkI2btyou7xR7TRafziKjs+Av2r/R4p1DVxpKNDhAGMap9depZ3uRk86tcD3rjzoINi0KepKV3Zopxn9K1kohzVOKisrk3/SgIZeUdGxo4Rr8IGpMq6/3vy8gyoXrHvRI5Chj1sri4s1hzWC6Kof4fSjpqbGUHmj2mm0/nAcdWz7CGu4dh4tfAqCR1hjaZxee5V2uhfd6dQCqBw8OKrDapd2mtG/koVyWN1EwKjWtGkd3w7X4KdNa2/0I0aY/7hB5YJ1L3EJpH8d+AgjrOpHOP2ora21NA7OlPoDJl2Fa+OhfWHbtmCNNaJxeu1V2ule4tJO/zJWERxWu7TT6v5rJsphjRN/Kgj/qi1JIUawdqwGb1WeN5V3053EI5Crt20DoChCW1Q/wulHeXk5VQbWkDSqnUbrD0uMtFZmaqcRe5V2upO4tHP3bsB52mlK/0oSQvpHTFKM4uJiaeVQt/CNMCX1+u3di+jSJep51YQXhRGMtpe2dv/883DBBZba5lSEEKullMV222EVKamdX3+NGDw46nmVdiqMELd2VlfDyJGW2uZUEtVOZ+VXUERHRzqMkpLgMACVh1URjdD2ohuV1krho6yszPn161g4wCzttPp6KJxB3NrpsLRWbmqvKiTATRh0EtSMbYVlOEx0FfZRXGztYLMp9Rtc6SoR7bT6eihcjsO0003t1VlXThEdgw09auJ3hSIR1AirwkeqjLAGkoh2umnESmEDDnNY3dRenXXlFNEx2ND1rrMd+Ogr3UMI0v3z68Zhoquwj7y8POfXb9Bh1aOdoVrhf92li7XXw6ko7dSJw7TT6v5rJiokwE0YbOh6Zh0GPvrKyNAGzpqb0zOEIB1CKEybWKJGWBU+rE46bkr9Bh3WWNoZLm+rP891ZuZGqqtTTzuiobTTAA5zWN2yaACoEVZ3Eegk6JxhGyswPPDRV1NTeocQpHoIhf9H5c47tb8JjYQ4THQV9mF1ShxT6o+jvUbTzlCtWLQo8HVVymlHLJR2GsBh2umWlFagHNa4qampsXeFCH8S4gQJfPSVmakvhCBV0RtC4VbM+FGpKSmhBmKKrno8mD7Es3KVEe00ZVVBgyOssQjVivHj219DZcppRyyUdsampn9/R2qnLat2xokKCYiTpC4YEI7W1pgirOcRRuijL0jfXISpnPS+uhrWrQOvr8cb+VEJakf+eKcoIQHp8HhQ0c6SJUsMlTeqnUbrD0scWQKi6UA4rWhfSXBJ2rV3pZ2Rj227Jjk52k6Haacp/StZSClTcisqKpKpCCABKffvj1pu1SopO3WSMiND+7tqVZIMVDiOwLaQlSXl1VcHt4dVq6ScNSt8GwltR98fOlpKkJ/+5dWI55s1SysP2t9Zsyz4UDYC1EgHaJxVm1HtvPDCCw2VN4op9e/d266dMUhUO62+HorkYaZ27ug1VEqQ7z/9RcTz2aGdyWyviWqnCgmIk7KyMl3pIKqqqvB6LRjIltFjWFM9pkihn8C20NICffsGJ0ePFpsVeGxDA1z1+aeUAbf8TkR8ZJXqjwcVwYwZM8ZQeb3aGW/9YTEwwpqodppir8IRmKqdP2ymDJh0qcdR2umm9po2DmtpaSnZ2dnk5eXRtWtXBg4cyCWXXNK2rrVR5s+fz/z584P2LViwgMG+5f+sJis/n9NOOy3i+8lq+CpW0RrMvK7R2kKsH+fAYzMy4F9sZD7Q2OyJ+EPufzxYUaHCAdKBiRMnGiofTjvNrD8sgQ5rYSH07KlthYUwdGj7vsJCrntoKN+1FLKFnmxq6cmN94aUCXecf19RERPffDNmx1W6aQ1mX1cztfM5djMfaGhylnaa0r+SRFrFsN55553ccccdAHz33XfMnz+fkSNH8txzz3HeeefZbJ0xyi67jC++/jri+8mIKVKxitZg9nWN1hb8ouo/V+iNTeCxPXrAVVdp+zO8IupNUEmJagvpghACGeOJj+31v/tu+/+bNkUt2tm3tbHNt+lh0ybE++8jFyyAFSvCdgKlm9ZgxXW1RDszPY7STqv7r5mkzQhrKP369WPmzJlMmjSJKVOmIKVk79693HTTTQwYMIDu3btz5pln8tVXX7UdU1payvXXXx80hL5s2TIAqqurufrqq/nmm2/Izc0lNzc3KF3Es88+y6BBg+jatSu//OUvqaurS8j+nt27xywT91rHOlFhB9ZgxXWN1Bb03NH7jw18invNtR71I6sAsPzHzpT6V65MvA6dSIjacZVuWoNV19Vs7bxzurO00y3OKqSxw+pnwoQJbNiwgTVr1nDFFVfwxRdf8M4777B582ZOOOEExowZQ1NTU1v5Rx99lOuuu67t9XnnncfatWspKSnhkUceYeDAgdTX11NfX0+p7zaqpaWF1157jY8++oja2lo++OAD7r///rY6xowZQ0FBQcRt4cKFHQ13QCNTsYrWkOzrqvfGJvAx2wMPRo5hVaQXYfXJafUnUZwWQtSOq3TTGuy4rvFo5/TfR45htQOr+6+ZpFVIQDj69OkDwA8//MDTTz/Nd999R69evQCYPn06c+fO5d133+Wkk04CYNy4cYwePbrt+OLiYhYuXMhtt90W9Tx3331328jruHHjgvIQLl261LjhDnBYUzmViZ049boGjlj4Y1idYpvCPpYuXWppHJwp9Qc21H79tFkwADk5UFAAO3Zo+0JfhysTaV9zM2zdytIuXZj4+usRO4dT+7fbcfJ1DdROfwyrU+yzuv+aiW0OqxCiO/AocDqwFZgmpezg6gshfg38FhgC7Ea7gb1NStlshh3r168HwONL5nvkkUcGvd/U1MT333/f9rp///5B7/fv37+tjkhkZGRwwAEHtL3u0qVLwiEBeh1W05aTi4CKVbSGRK+rFd974IiFxxs9DkthDU7RzUBcMcIayNq1uooZ7kOLF8P48SwcPTrmAUo3rcGJugnB2unNcpZ2qhFWffwVaAR6AUcDLwshPpJSfhpSrjNwPfAucADwT+Am4G4zjHj22Wc5+OCDGTp0KABffvllkHMZylqf2B177LFtr3/+858D7U6vUc466yzeeuutiO/PmzePiy66KGjfhg2RHVZ/p+vRo319a6NB6FY7uoroBF5/MPZdWDWpo6QEjs7pgmf/Hu57QDBCtQs7cIRuBjJ27FhDycf92mlV/bGorg7fH0L7nOE+lJkJwJkrVzFqttJOOwj93TLyO2blZLiSEjgmw4toaWbe/3kodlC7MLt/WUoiSVzj3YAuaKI7NGDf34G7dRx7I7AkVrnQ5NejRo2SFRUVba/XrVsn77rrLpmRkSH/7//+T0op5cSJE+UFF1wg169fL6WUcseOHXLx4sWyrq6urY7c3Fz5xhtvyObmZrlw4UKZlZUlv/76aymllK+++qrMy8uTu3btajvPihUrZEZGRpAt06dPl6eeeqo0yqpVsi35dX7G9fLYY38m9+3bJ/cHLCIQmKzY65XS4zGehFgtOmAvocmqs7ONfReWJp8+/nit4upqEyt1F9i0cEAydFOG0c5Y/POf/zRU3ihm1B+oneH6UajmXX11HH3olVekBDlDHK200wZCv8N584z9jlmetL9nT63yLVtMrjgxrO6/gSSqnXZNuhoKtEgpawP2fQQcruPYk4HQ0QQAhBBlQogaIUTNjz/+2OH9iooK8vLyyM/P5+STT+arr75i1apVjB8/HtDyAw4bNozS0lLy8vIYMWIEzz//PCJgKbXLL7+cOXPm0LVrV2bMmMHixYsZOHAgAKeccgqjR49mwIABFBQUsNLkmamBcTC7W+by/vsr6NSpE8OGDQsq458p6V+91WgQuprFai+B17+pyfh3YenkA38oSpTlBRWWYYluQmztjEapxc83zag/KP46TD8K1TyIow/5RliLZK7SThsI/Q4XLTKmnZZP2mpt1f7G+STWKqzuv2ZiV0hALrArZN8uIC/aQUKIy4Bi4Ipw70spK4FKgOLi4qBn5lU6lKNz587MnDmTmTNnRizTs2dP5s6dG/Y9r9fLokWLOuxvbg4OGysvL49pSzgC29XAnA089e/CoEcW/jWP/Tmys7Jg7lzYts3Y46lY+eUCz5eMsIF0C08IvP4ZGZpv2NysX0QtnXzgUNFNEyzRTYiunbEoLCxMPCbf4voD+024ftSjh9bPPB7t/UmTtM1QH/I5rBewKqbTo7TTfEJ/t8aPh7feiv075sfySVsO1U6r+6+Z2OWw1gP5IfvygYhXTQgxDi3+6jQp5VYLbdOFf9RVJnG2fmAHeu6ZVopCnNVTT9UmrAoBY8fCLbcELyM3W2dclZ6Om6zk13Ym2bZL7EtKtBuNRYs00R0xwrgdoZMPzPoswrcynFQjrHbgSN00+mNnVDvN+DENbPOhGlJdrcX6t7Ro2jllSvv7/jhIXdrpc1h/OKyYBy9OX+20UzeXL4cnn9Rejxhh3AENN2nLNO3cuRMA6TCH1S3OKmB7DOuQgH1PEiEWCzgT+BE4Xu85jMZh6SEwDhZfPFSyaTvvd98F7Z81qz1eFaTMzGyP2bEiJtXyeJ8knycUO+N4jZx71SrtmsQqY9ZnaWt/q1fHX4nLwf4YVst0U8ahnfPmzTNU3qh2Gq3f6HlN086aGilBzuvTJ6odqaydds9/cIV27t4dfyUWYFb/0kOi2mmLqy+l3AMsBmYIIboIIX4CnIs2gSAIIcQpwD+A8VLK/ybX0mCqqqralna1Hf/jBR+lpcFPGlpa2mN2rIhJTVaSZruSbNsZx6v33P4RlDvv1P5GSkZtyWdx2ChBOuBU3QzMKe3G+k3TzqwsAGr27Il5vlTVTrvnPyjtNI7V/ctM7Lxy1wCdgC3A08BkKeWnQvz/9s4/SK+rvO+fR7ur3cWWkS03GCUolClKg8OA0XrI1gky4BJIpIEp007GDHZwsTxOIQHDTHHGwipmLMdTBaeJIVoa/xCNYzJTk8SmJoRgObjemVTyAK3TRDYzxgVZBixhJGe1K61O/7jv1V69en+d+94f59z3+5m5I+19zz33Ofc953uf9/x4jm0ws2NmtqGVbjvwcuB/tM4fM7OHa7I5HNoc1tlZuPPOZFRq1SqYnFwRqLKE6+qr4dpryx1qGmT7uzKoczea9N6rViXHunWd0w0qpqWURVMC6iI43Zybmysj28ryL0w7W1MC5nqERUxpqnbWvYtXFNoZmMNadvsqlGG6Z0M+ypgSkIW6pwQ8/XTHz7sNcwwy/DEoRQ+7hEqdtu/evRKWrNszruN7OF3/vv3t4TKKGGqaElDV4audmzdv9krvq52++ee979Da+fTTzoHbPDXV8x5N18667Q5eOzNhKEOgqPY1CMNq58hvzRotbT2sKd12+sieH3YSeadfp4MsLvjwh+Gb30wWEm3b5n/fqqlrN5r5+WTB1alTydHtGfusah2mLO0B1QH1sIrT5I16Ekr+KUNrZ6uHdce553a9xyhoZ527eEWhnYH1sFbVvopADmusdHFY+zE/D29964oQPvKIX2NMQ2eNt2pOr2GSrDgfPw63356c/+pXk39T4R2l0Cv9yEZ7OHVqJcxOt2dc9laE7S/O0wQmuqI+0l0CY81/UPpqZ6uBbHSdox9IO8tF2pmPUNrXIMhhzcnu3bvrNSCnw7pnT9KgIfl3z57Be16zDXBsLJmDdcklK/N/2q/JxsVzbiXmPCS/grdtqzdsVYikL6pUcK+4AnbsqC/sTXuP0K5zX8G5x55XD6s4zczMDAcPHhw4va92+uZfFt20M9XNKy6Z4FJg5vBh2q1t186tW+Gii7rfS9rpT+jaebrWB+awhtK+BkEOa0621T0uk9Nh7YZvA0z5yEe6X5Mddvnxj1d6CSAZ2mrPs9cQ2ajQHvy6LMGFwZ59uz3Xn38e08eeD050RX34vux8tTPkl2lWN++YmOB54OA555yVLtvWnIMvfzmR8Hvv7ay10k5/gtbOCce29L0Z2I/9kNtXO3rrxEqXYad+XHVV0ojMVnZ0gf6rJrPDWemKSei/0nJ2Fm68EX73d2H3bnjHO5J/03dW3atK6yQNSJ4NqVLlyt723X06Pft2e6YntTWrOJNdu3ZFnf+gdNLOrG6+tJTMYd21sHDGde3auWpVkr7fCnVpZ3ei086vhaubobSvQVAPa07SUBC19bTm7GGdnU0Esn3ov9d2rJ2mAqSO7r33Dr713bZtZy8YKH07vEDp1aNdxaKFdHefU6eS7/SOO7rfM2vPXGu3lm3qYRUtfHtofLUzlB6gbtqZ6qZNTMBxOJgZguo2jSo7MjWIoyntXCFK7Tx5ijR4VGhr5kJpX4MghzUn1113HRCfwwqdG3Uv8WufCrBhw8rnRQhmnatK62LY4byiIj2cOpX86H/hhcGuu+5Hye6eclhFim8Pja92htQD1K5VZ+jm5jH4JWOXc0nDHhvrqJ3btuXbbnkQe0aBYbSziEVqubTz1CmuA3AuOIc1pPbVDzmssVLwHNZe9Op9LUIwR3Gla69n2o8iFlsMc38gyKEtUQ+bNm1i//790eY/LGnb27vXePP4BJeeWGL/iRMwNta1nRXlaEo7B9euohap5bp/he9rX0JvX1nksMbK5s3JpKipKVhYWPkJ335ukDTACcZ59bEprmOBcZY5OQ3jk0m62YUFfsIySxPjjE1PMfmu7nmdXIYlN87Yy6aYXO5v14ljC1x8bJmLgZOMs3jBYNf1K0+Rz6aMvHyeafu5SxaWObSUPK/jC1Oc/9YFmPCzYXZqihfHFlgeW2YcGP/VAcuTcv/9yQQ7MfLEvtPVsGQdoQ8vTzDHEpw4kbRzj2F7X+dzVKME5J0KUdQitVz3D9hhDb19ZZHDGhPZGebHjhWa9QTwyuyJhdbRYrx1cLx3PoOmy953Invi8GDXNQHfZ5Uy1TpOs9g6PDnj2S/0SNiJ3/mdZOVB3dEyRO2sWbMm6vyHJesInWCCNZA4rC0G6U3N43zWPTReJ3l6qPP0jHZ7Tiu96mf+3ZWAHdbQ21cWOawx0W05qRB1kAaEFCPNli1bOHDgQLT5D0vWETqxPMEW4MDSklceeZzPuofGq6Io59q3Z7TXc/J+hgE7rKG3ryxyWGOi5LglgwTK6jVzsdP1g8x0zF7nMzMy7/2qpttz7WZr3ufR6dpez2jo55cGhBQjTdkvu9BfpllH6OWfWc2BH8IffuYEm96TfD6Ig5TH+ax7aLwK5ueTsp04kYSQ+uxne/9G7ufc9uqZbb+213PyfoYBO6yht68s5nLG8wydmZkZt2/fvtLyt9aik6qf3+n7/uzPJgtf1q6FI0dWtmCZmjrzXPvfndK0nVs8usgLh+E4U7zIWtZyhNUsssQU/+y1azl3qXtei0vwT26KiQt7p8tjV/t17Xb+y4uOME2+vLxs8Mzrh99JbAVYbNl6PkdYf8Eik+ed/fwOHp7ix63nvv6CRSZXD2bDAlP8w6G1vJwjTLHIugvg1Oqzz6X3XDh0hCOHFnFw9nfb5X72zDNJ/csGhBwxzGy/c26mbjvKwlc7d+zY4bUfua92+uZf1H3zsHjhK9n5wiGu4ac5yQQv0rntdWrHC4eOcOKlRSbOmWL6ovJ0qZNO9LKrar3MnvvhsSn+37HE1snWO+inuryDeupZHxs6vUtWLXV/v7Q/w/Mv6vOdLSxgP/gBAO7xx4P6hVBU+xqEYbVTPaw5qd3RbzkOZfB7O2H79uTXY7oY3Lkk5twtH+i91maydVTBJPDdzK/i6XA04Aw+eT380R+deW5sDG75+NnPMvvsu6Xpxh1drj0+D1/Zmzyj9ZlnNA18M/P8/vkAz6+ZP29FldSunWUxP8/qFw4B8Cq+f/bnh+k5R3+6dXAUOFS8edn7vDHzt/Wxq04ubB1n8FTntFO0rcPokq4Tq9uvPZSMNmXPWeY7OesZHqLvd3a61m/eDI8+GpTTGgtyWMVZZIenxsYSp/Xkyep3Uxlk7lIMcQivugruvntlX/Beu6MME24qTwidGJ6fCJuye2eq6v0ZmtYagx21GjEYIU6d6oSPncOUqdu1vfLMfb8TJ4KahxFN+0IOq+hA+9wogD17qrWhjoUBZa2cnZ2FRx5J8l63Lgk03WuOVb95ab1Wro7izjeiXjZu3FjqPLiy8y+Myy/HxsfZePIk/9glSRWOYh1z+4eZd++Td7/8+9nR6/Myy3AGExNB7aMbTftCDmtuNm3aBBBNwF1fsj1v8/MrW7Dee281zmO3Se1lOJXz84lDftddyf3KcJB9ejL7LQzo5ciX3WPa9Hov/HnooYe80vvWId/8a2N2Fv72b3nok5/EnnoKzDg2sZYfPpWsATDoP9exgLmih44n8znTdQfTF63llZPJPM3FE+SfI9tlPuwPltayfDiZY1pGGc3jOuuRV795p+b5nH3Ls+nwYZieZv/DDwfVmxBN+0IOa26eeOKJuk2ojDpWlXYa3i6j1zXN8/jxZLgewl45W/cK31Gq92Iwjh496pXetw755l8rs7Mcve02aDnlf9A+r/y3yt9v45l2nXwAniFz7mRyrlDtbJ2rqox5OGuOf8V2PmGWfAGBvVhial/aEFz0JXUex8byz2Odn4edO8/c+6AX6fD2LbesOKadnLVhSfNMnVWz6ufq+lDEdyFEkWwrOVpE2fkXTdZeaWc4SDs7E1P7UlirnNQe1qri+w4zFF9Uz2iZPaxLS8nOox/4QLJIqsgtFIumzvvXVf9CQmGthkPa6XettLM4pJ31MrR2OucaeWzatMmVCckoSKn3COm+w3Drrc6NjTkHyb+33po/r8cfT65//PHi7PPJ8/HHnZueTsoxPT28HUWVp4zn0okY61/RAPtcABpX1uGrnTfccINXet865Jt/UffNS1H2OiftLOK+VebVi1C1s8j62o9htVNzWEUufH6pDhOqqZ2iFxX5/uIucg5pyL0nQgzK+vXro86/aHrZ66s3oWpnthyDzgMtSjuL1DtpZ1ztSw6r8Ma3kYcabqnfXtGd7C3yBVKUgNe9EEuMNh/72Meizr9outmbxzkKUTvz6CYUp51F6p20M672JYc1J9dee23dJtRGnkYeYoD6XqGzuglykS+QogS8SCe6H6Nc70Vn1q9fz8GDBwdO71uHfPOvm2725nWOQtPOPLoJxWlnkXon7Yyrfclhzcnc3FzdJtRGlY28TLqVo9+LpagXSFECXmUvzCjXe9EZ3wVavnWozAVgZdDN3lHXTShGO4vUO2lnXO1LDqvwJsRhKh+yw1adylHli6VI5ze270E0gwMHDpQ6D67s/Iumm72x6yasaOcdd5y9Y1+Mull0XjESVfsaZsXWMAdwAfAl4CXgu8CVPdJ+FDgEvAjcBUz2y7/sKAH79u1z+/btK/UenSDQlYa9qGoV5iD3GnSlans+VZYhZOqq9yFBjVECytZNl0M7N2/e7JXetw755t+NqrSzKHudi087O+Uh7UwIVTuLrK/9GFY763RY/xT4InAu8EstUb24Q7pfAZ4HLgbOB/YCt/XLX2GtwqDoMFDD3itPmJgqyxA6sdW/MqjZYS1VN520Mxiknc0itvpXBsNqZy07XZnZOcB7ge3OuWPOuceAvwTe3yH51cAfO+eedM4dAW4BfqMyY8VQdJrX5LtzyzD3aifPbidl7BLTibKei2gGoeqmdro6k6LslXYOjrQzPzG1r7rmsG4Elp1zBzLnvgVs7pD2YuAv2tK9wszWOedeKNFGUQDt85rWrSsv7l23OVTtoVZ855FVMTdL8QDFAASpmzMz5W76VXb+RVOUvXVrZ7tuSjubSUztqy6H9VySoawsLwJrBkib/n8NcIbwmtk2YBvAhg0bCjFUnI1P8Ot2kSsz7l0nQe0mZj73rGKxhOIBigEoRTdhOO1UD+uZdLPXd9OAOrUTuuumtLNZxNS+6nJYjwHntZ07Dzg6QNr0/2eldc7NAXOQ7Ic9vJminbzBr7NpyvzF3X6vosQszTcdesqKbxH7Uzcl5I0olVJ0E4bTzjVr1nD0aMdsC6Hs/Iumk715ewHr0s6dO4tzAqWdYRNT+6rLYT0AjJvZa51zT7XOvQF4skPaJ1uf/Vkm3fOaDlAPwzqAVYd2KVLMOr10oJjhqCaEvBGlE6Rulh10PJag5ilFbhqQpUqNKNoJlHaGS0ztqxaH1Tn3kpk9AHzKzD4IvBF4N/CvOiTfA9xjZn8CPAfcBNxTla3iTIoQsirj3hUpZt0WEPi8iHr1KIx6PEDRm1B1c+/evWzdurWMrCvJv2g62VuUA1iVRhTtBEo7wyWm9lXnxgG/SRIb8Ackc6qud849aWYbgL8HXuece9Y59xVH2HxOAAAOjElEQVQzux14BJgG/jtwc11Gp8S0O0SRxPhrtigx6/bSGfRF1ITFAaNa7wMiON2cm5vzeuHl2RkrlhcqdLZ3lHUTpJ0QrnbG1L4sCY3VPGZmZlyoFWQYzAyAEL+3IuYjhXAP3/sPatPOnbB9e9KjMDYGt9wCN95Yvs2iWMxsv3MunqW1nkg7q0faKe0cBYbVTm3NKgqhil/AZd9jEPFs73XweQlocYBoIldeeSX33XdftPkXja+90k5pZ53E1L7ksOYkDQUxNzdXsyVh0D5Hac+e4n/Nlxm+JI+g+14T47BgO6r3op0tW7Z4pfetQ775142vvdJOaWedRNW+htkmK+RD2wuWQ7d9obNb8K1e7dzkZPHb8ZW5zV+ebQfzXONc3Htr113/QoAat2at4pB2Fk+vNi/tHHyb11h10zlpp3PDa6d6WMXA9PpVnP0F/Oyz8PnPF/9rvuhf2dkhqTxDTnmuSZ/h4iKsWgV33gkRxW0W4izMjORdFGf+RdNub7/exNi0s30ovwrtzD7DsTG45hq46qo4e1pDI6b2JYdVDEy/YaVsgOh77y1nvlF6vzQsSl7B6vQS8RX0PC+BvXsTZ/XUqeT40Ifg9a+X8Ip4KftlF8vLNKXd3kGG42PRzm7Od9namX2Gy8uwe3fynGKMFhAaMbUvOaxiYAb9VVzmfKOiFg90eonceKP/ogDf0C+XX570rJ46lfy9vKytBEXc3HfffVx55ZXR5l807fb69CaGrp3dnO88C6p8tDN9hsePQzKRQNuwFkVM7WtV3QaIeEjF9JZbBpsk3+4AFkG3ANS+pAI4Ntb5JZKK+/btyWfXX5+cG5bZ2WQawMRE4rhOTmrFq4ibhx56KOr8i6bdXh/dTNOHqp39dBNWtPOmm+Atb4Ei1hilz/C66xLN7HV/4UdM7UtxWHNSV0y/kGMJVkGR4Vl69QJk4/4BmMHUVHFDUHXHRMzLqNc/UBzWYZF21kNR2tlPu3buTJzVdBRpYgIefbTYqATSzjhRHNaaeNOb3lS3CcFRhZAUOWTWa0iq7CGoWLcSVL0X7WzdupUHH3xw4PS+dcg3/7rJY29M2tlPu8qe9iTtLJaY2pcc1pzs37+/bhOCosqt86oQrFTc9+yBu++Gkyc1BAWq9+JstnmGufCtQ775142vvU3UzjvvTBaULi9r2lNKqNoZU/uSwyoKoczA1FXS3tMxO5uET4lxCEqIKri8ZG+k7PyLxtfeJmrntm1J9BPpZvjE1L7ksIpCaMLWed16OmIdghKiCtavX8/Ro0ejzb9ofO1tunaKsImpfSlKQE7M7PQkauG/EjZEsj0di4uwY0cxkQGahOq9aMf3Zedbh2J5mab42ivtHA1C1c6Y2pccVlEYZYVjqYq0pyNdMPC1ryW9BhJeIbpT9t7ooe293o889ko7RV3E1L7ksIqRZX4+CcGSimra03HFFSvCO0ysVyFGgTJDYFWRf9HEZm8epJ3NIab6qjisOVEswbjptTK3ylW7saH6pziswyLtjBtpZz5U/4bXTvWwipGk164v3eaUtfcqCCEUJaCd2Oz1RdrZLGKqr4oSIEaSfitzO+2NrZ4DIc5mx44dUedfNLHZ64u0s1nEVF/Vwyo60vRfxL4rc4vYh1uIJrJx48ao8y+S+Xn4q7/a2FjdBGln04ipfamHNSe7d++u24TSGJVfxD5xApsQK7EImlzvRT5mZmY4ePDgwOl965Bv/nWR6ubCwgy///sHG6ubIO3MQ6jaGUv7AjmsuYlpOzNfmrLzSpEUtQ937DS53ot8+L7sfOtQLC/TVDfhoHQzg7QzIVTtjKV9gaYEiA6kv4jHxkb7F3E7scdKFKIMdu3aFXX+RZHqptku6WYb0s5wiaV9gRzW3MzNzUUVcNeHJuy8IsqhyfVe5MO3h8a3DsXSA5Tq5mWXNXs6gMhHqNoZS/sCxWHNjWIJilFE9U9xWIdF2ilGEdU/xWEVQghRI5s2bYo6/6KJzV4x2sRUX+WwCiGEyE3Zw5whDqP2IjZ7xWgTU32VwyqEECI3a9asiTr/oonNXjHaxFRfK3dYzewCM/uSmb1kZt81syt7pL3azPab2U/M7HtmdruZKRSXEGLkCFU7t2zZUka2leVfNLHZK0abmOprHT2sdwJLwCuA9wGfM7OLu6R9GfAR4ELgzcDbgY9XYaQQQgRGkNp54MCBMrKtLP+iic1eMdrEVF8rdVjN7BzgvcB259wx59xjwF8C7++U3jn3OefcN5xzS8657wN/AlxWncVCCFE/IWtn2XuRx7TXOcRnrxhtYqqvlYa1MrNLgMedc9OZcx8HNjvntg5w/Z8D/+Cc+0SXz7cB6XYSvwD8n+GtDpYLgR/VbURJNLlsoPLFzs855yqd+CXtLIym102VL26aXr6htLPq+aDnAi+2nXsR6FsAM/sAMAN8sFsa59wcMNdKv6/JsRKbXL4mlw1Uvtgxs/KClHZH2lkATS4bqHyxMwrlG+b6QqcEmNleM3NdjseAY8B5bZedBxztk+97gNuAdznnmvzrQwgxgkg7hRCiN4X2sDrnLu/1eWse1riZvdY591Tr9BuAJ3tc807g88CvOef+d1G2CiFEKEg7hRCiN5UuunLOvQQ8AHzKzM4xs8uAdwNf6JTezN5Gsljgvc65v/O8XTzRcPPR5PI1uWyg8sVO5eWTdhZGk8sGKl/sqHw9qHTRFSSxBIG7gH8NvAB8wjl3X+uzDcDfA69zzj1rZo8Avwwcz2TxDefcuyo1WgghakbaKYQYZSp3WIUQQgghhPBBW7MKIYQQQoigkcMqhBBCCCGCJlqH1XNf7Y+a2SEze9HM7jKzySptzcOg5atyz/Ai8fn+Mtd8vRXmp1HlM7PXmNlDZnbUzH5kZrdXaasvHnXTzOzTZvb9Vtvb22Mr0WAwsw+Z2T4zWzSze/qkbay2tNI2tnzSzvBosm5Cs7WzCt2M1mFlwH21zexXgE+Q7KX9auA1wH+qzszcDLpveGV7hheMz77omNn7qH6ji2EYtH6uBv4a+DpwEfAzwH+r0M48DPrd/VvgGpLFPxcA83RZ1R4YB4FPkyxw6krTtaXp5UPaGSJN1k1otnaWr5vOuegO4BySL31j5twXgNs6pL0PuDXz99uBQ3WXoajydbj2BuDBustQZPmAlwMHgF8EHDBedxmKKh/JdpjfqNvmksr2H4E/y/x9MXC87jJ4lPXTwD09Pm+0tjS9fB2ulXZGUrbYdDNH+aLVzjJ1M9Ye1o3AsnPuQObct0i+1HYubn2WTfcKM1tXon3D4lO+dt5Cj2DigeBbvluBzwGHyjasIHzK94vAM2b2cGtYa6+Zvb4SK/PhU7b7gX9hZhvNbAK4GvhKBTZWRdO1penla0faWS9N1k2Qdqbk1pVYHVaffbXb06b/77sHd43k2jfcVvYM/88l2VUUA5fPzGaAy4A/qMCuovD5/n4G+HXgvwDrgS8Df9Ea8goRn7I9B3wD+EdggWSY66OlWlctTdeWppfvNNLOIGiyboK0MyW3rsTqsPrsq92eNv1/zz24a8Z733CLa8/wgcpnZquAzwK/7Zw7WZFtReDz/S0AjznnHnbOLZG8MNcBP1+uibnxKdvNwKXAq4ApknlKXzezl5VqYXU0XVuaXj5A2hkQTdZNkHam5NaVWB3WA7T21c6c67av9pOtz7LpnnfOvVCifcPiU77snuFbXRx7hg9avvNIej2+aGaHgP/VOv89M/vl8s3Mjc/3922SuWWx4FO2NwBfdM59zzl30jl3D3A+8LryzayEpmtL08sn7QyLJusmSDtT8utK3RN0h5jYez/wpyQTmS8j6Va+uEO6d5LM33kdyRf+dQaYgF/34VG+t5Fs0/iWum0uunyAkawATY9LSUTqp4HVdZehoO/v54B/Aq4AxkiGfb4Tcvk8ynYz8BjJithVwPuBl4C1dZehT/nGSXo1dpIsipiiw2KVEdCWppdP2hnY0WTd9CxfdNpZhW7WXsghHs4FwJ+3vsRngStb5zeQdDlvyKS9AXge+AlwNzBZt/1FlQ94BDjZOpceD9dtf5HfX+aaVxP4Stc85QP+DfB0q37u7SRgIR0edXOKJIzLc62yPQG8s277ByjfjlY9yx47Rk1bml4+aWd4R5N106d8MWpnFbpprYuFEEIIIYQIkljnsAohhBBCiBFBDqsQQgghhAgaOaxCCCGEECJo5LAKIYQQQoigkcMqhBBCCCGCRg6rEEIIIYQIGjmsQgghhBAiaOSwCiGEEEKIoJHDKoQQQgghgkYOq2g8ZjZtZt8zs2fNbLLts/9qZstm9ut12SeEECEi7RQhIYdVNB7n3AJwM/Aq4DfT82a2E/j3wIedc/fXZJ4QQgSJtFOEhDnn6rZBiNIxszHgW8BPAa8BPgh8BrjZOfepOm0TQohQkXaKUJDDKkYGM9sCPAj8DfA24A+dc79Vr1VCCBE20k4RApoSIEYG59xDwBPA24EvAr/dnsbM/oOZ/Z2ZHTezvRWbKIQQwSHtFCEwXrcBQlSFmf074I2tP4+6zsMLzwG3AZcCs1XZJoQQoSLtFCEgh1WMBGb2DuALwJeAE8A1ZvYZ59z/zaZzzj3QSr+heiuFECIspJ0iFDQlQDQeM3sz8ADwP4H3ATcBp4CdddolhBAhI+0UISGHVTQaM/t54MvAAeA9zrlF59x3gD8G3m1ml9VqoBBCBIi0U4SGHFbRWFpDU18FXgTe5Zz7SebjTwELwO112CaEEKEi7RQhojmsorE4554lCXjd6bPngJdVa5EQQoSPtFOEiBxWITKY2ThJuxgHVpnZFHDKObdUr2VCCBEu0k5RNnJYhTiTm0i2IkxZAB4FLq/FGiGEiANppygV7XQlhBBCCCGCRouuhBBCCCFE0MhhFUIIIYQQQSOHVQghhBBCBI0cViGEEEIIETRyWIUQQgghRNDIYRVCCCGEEEEjh1UIIYQQQgTN/wf2C5N5F5BcGgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 792x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 13,
+   "id": "f200c210",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.tree import DecisionTreeRegressor\n",
     "\n",
@@ -1693,22 +1598,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 792x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 14,
+   "id": "afabbb94",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
     "tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n",
@@ -1742,7 +1638,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "4a04f64c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Pros and cons of trees, pros\n",
     "\n",
@@ -1758,8 +1657,16 @@
     "\n",
     "* Can model interactions between the different descriptive features\n",
     "\n",
-    "* Trees can be displayed graphically, and are easily interpreted even by a non-expert (especially if they are small)\n",
-    "\n",
+    "* Trees can be displayed graphically, and are easily interpreted even by a non-expert (especially if they are small)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8de95ca3",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Disadvantages\n",
     "\n",
     "* Unfortunately, trees generally do not have the same level of predictive accuracy as some of the other regression and classification approaches\n",
@@ -1778,9 +1685,16 @@
     "\n",
     "However, by aggregating many decision trees, using methods like\n",
     "bagging, random forests, and boosting, the predictive performance of\n",
-    "trees can be substantially improved.\n",
-    "\n",
-    "\n",
+    "trees can be substantially improved."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5dd3105",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods\n",
     "\n",
     "As stated above and seen in many of the examples discussed here about\n",
@@ -1802,22 +1716,32 @@
     "\n",
     "4. Boosting methods, from adaptive to Extreme Gradient Boosting (XGBoost)\n",
     "\n",
-    "We discuss these methods here.\n",
-    "\n",
-    "\n",
+    "We discuss these methods here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64c4528d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## An Overview of Ensemble Methods\n",
     "\n",
     "<!-- dom:FIGURE: [DataFiles/ensembleoverview.png, width=600 frac=0.8] -->\n",
     "<!-- begin figure -->\n",
     "\n",
-    "<p></p>\n",
-    "<img src=\"DataFiles/ensembleoverview.png\" width=600>\n",
-    "\n",
-    "<!-- end figure -->\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
+    "<img src=\"DataFiles/ensembleoverview.png\" width=\"600\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
+    "<!-- end figure -->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1190e743",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Bagging\n",
     "\n",
     "The **plain** decision trees suffer from high\n",
@@ -1830,9 +1754,16 @@
     "\n",
     "**Bootstrap aggregation**, or just **bagging**, is a\n",
     "general-purpose procedure for reducing the variance of a statistical\n",
-    "learning method. \n",
-    "\n",
-    "\n",
+    "learning method."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24b1edfe",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## More bagging\n",
     "\n",
     "Bagging typically results in improved accuracy\n",
@@ -1855,29 +1786,28 @@
     "trees. A large value indicates an important predictor. Similarly, in\n",
     "the context of bagging classification trees, we can add up the total\n",
     "amount that the Gini index  is decreased by splits over a given\n",
-    "predictor, averaged over all $B$ trees.\n",
-    "\n",
+    "predictor, averaged over all $B$ trees."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a434b379",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
     "## Simple Voting Example, head or tail"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x252 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 15,
+   "id": "eb98cf10",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "heads_proba = 0.51\n",
     "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n",
@@ -1896,31 +1826,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "f77f79a5",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Using the Voting Classifier"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LogisticRegression 0.864\n",
-      "RandomForestClassifier 0.872\n",
-      "SVC 0.888\n",
-      "VotingClassifier 0.896\n",
-      "LogisticRegression 0.864\n",
-      "RandomForestClassifier 0.872\n",
-      "SVC 0.888\n",
-      "VotingClassifier 0.912\n"
-     ]
-    }
-   ],
+   "execution_count": 16,
+   "id": "732b7fb7",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.datasets import make_moons\n",
@@ -1969,29 +1891,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "7d845e84",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Please, not the moons again! Voting and Bagging"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
-       "                             ('rf', RandomForestClassifier(random_state=42)),\n",
-       "                             ('svc', SVC(random_state=42))])"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 17,
+   "id": "67e23e21",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.datasets import make_moons\n",
@@ -2015,20 +1931,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LogisticRegression 0.864\n",
-      "RandomForestClassifier 0.896\n",
-      "SVC 0.896\n",
-      "VotingClassifier 0.912\n"
-     ]
-    }
-   ],
+   "execution_count": 18,
+   "id": "84df430d",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
     "\n",
@@ -2040,23 +1949,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
-       "                             ('rf', RandomForestClassifier(random_state=42)),\n",
-       "                             ('svc', SVC(probability=True, random_state=42))],\n",
-       "                 voting='soft')"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 19,
+   "id": "33abb82d",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "log_clf = LogisticRegression(random_state=42)\n",
     "rnd_clf = RandomForestClassifier(random_state=42)\n",
@@ -2070,20 +1969,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LogisticRegression 0.864\n",
-      "RandomForestClassifier 0.896\n",
-      "SVC 0.896\n",
-      "VotingClassifier 0.92\n"
-     ]
-    }
-   ],
+   "execution_count": 20,
+   "id": "1c736721",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
     "\n",
@@ -2095,15 +1987,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "dba7c494",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Bagging Examples"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
+   "execution_count": 21,
+   "id": "725c531d",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "from sklearn.ensemble import BaggingClassifier\n",
@@ -2118,17 +2017,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.904\n"
-     ]
-    }
-   ],
+   "execution_count": 22,
+   "id": "b322dc91",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
     "print(accuracy_score(y_test, y_pred))"
@@ -2136,17 +2031,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.856\n"
-     ]
-    }
-   ],
+   "execution_count": 23,
+   "id": "ddf7f820",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "tree_clf = DecisionTreeClassifier(random_state=42)\n",
     "tree_clf.fit(X_train, y_train)\n",
@@ -2156,22 +2047,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 792x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 24,
+   "id": "bb3a6ccb",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from matplotlib.colors import ListedColormap\n",
     "\n",
@@ -2204,7 +2086,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "ac6fb3ca",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Making your own Bootstrap: Changing the Level of the Decision Tree\n",
     "\n",
@@ -2214,64 +2099,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Polynomial degree: 1\n",
-      "Error: 0.06380941468319971\n",
-      "Bias^2: 0.05160313473529168\n",
-      "Var: 0.01220627994790804\n",
-      "0.06380941468319971 >= 0.05160313473529168 + 0.01220627994790804 = 0.06380941468319971\n",
-      "Polynomial degree: 2\n",
-      "Error: 0.043464037468677004\n",
-      "Bias^2: 0.02659851591375224\n",
-      "Var: 0.01686552155492476\n",
-      "0.043464037468677004 >= 0.02659851591375224 + 0.01686552155492476 = 0.043464037468677\n",
-      "Polynomial degree: 3\n",
-      "Error: 0.020716391693769383\n",
-      "Bias^2: 0.01159033914386312\n",
-      "Var: 0.00912605254990626\n",
-      "0.020716391693769383 >= 0.01159033914386312 + 0.00912605254990626 = 0.02071639169376938\n",
-      "Polynomial degree: 4\n",
-      "Error: 0.02063627410934057\n",
-      "Bias^2: 0.0117496656370668\n",
-      "Var: 0.008886608472273775\n",
-      "0.02063627410934057 >= 0.0117496656370668 + 0.008886608472273775 = 0.020636274109340574\n",
-      "Polynomial degree: 5\n",
-      "Error: 0.02087627881701288\n",
-      "Bias^2: 0.01349183949256158\n",
-      "Var: 0.007384439324451296\n",
-      "0.02087627881701288 >= 0.01349183949256158 + 0.007384439324451296 = 0.020876278817012876\n",
-      "Polynomial degree: 6\n",
-      "Error: 0.02069601123831537\n",
-      "Bias^2: 0.013918526350129823\n",
-      "Var: 0.0067774848881855445\n",
-      "0.02069601123831537 >= 0.013918526350129823 + 0.0067774848881855445 = 0.020696011238315368\n",
-      "Polynomial degree: 7\n",
-      "Error: 0.022964339924731444\n",
-      "Bias^2: 0.01550381208433455\n",
-      "Var: 0.007460527840396904\n",
-      "0.022964339924731444 >= 0.01550381208433455 + 0.007460527840396904 = 0.022964339924731455\n",
-      "0.5148389267750961\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 25,
+   "id": "1564f70c",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "\n",
     "import matplotlib.pyplot as plt\n",
@@ -2322,7 +2156,7 @@
     "    print('Var:', variance[degree])\n",
     "    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n",
     " \n",
-    "mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**2))\n",
+    "mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**2)\n",
     "print(mse_simpletree)\n",
     "plt.xlim(1,maxdepth)\n",
     "plt.plot(polydegree, error, label='MSE')\n",
@@ -2332,41 +2166,9 @@
     "save_fig(\"baggingboot\")\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 5
 }
diff --git a/doc/src/week44/week44.do.txt b/doc/src/week44/week44.do.txt
index e89d852f3..ae324a76b 100644
--- a/doc/src/week44/week44.do.txt
+++ b/doc/src/week44/week44.do.txt
@@ -6,8 +6,8 @@ DATE: today
 !split
 ===== Overview of week 44 =====
 
-* "Thursday: Wrapping up PCA from last week and basics of decision trees, classification and regression algorithms with video of lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober29.mp4?vrtx=view-as-webpage" 
-* "Friday: Decision trees, voting models and bagging with video of lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober30.mp4?vrtx=view-as-webpage" 
+* Thursday: Wrapping up PCA from last week and basics of decision trees, classification and regression algorithms
+* Friday: Decision trees, voting models and bagging
 
 
 Geron's chapter 6 covers decision trees while ensemble models, voting and bagging are discussed in chapter 7. See also lecture from "STK-IN4300, lecture 7":"https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf". Chapter 9.2 of Hastie et al contains also a good discussion.