diff --git a/doc/pub/week44/ipynb/Datafiles/cancer.dot b/doc/pub/week44/ipynb/Datafiles/cancer.dot
index 51d41287e..0a9d326a0 100644
--- a/doc/pub/week44/ipynb/Datafiles/cancer.dot
+++ b/doc/pub/week44/ipynb/Datafiles/cancer.dot
@@ -10,7 +10,7 @@ edge [fontname="helvetica"] ;
 2 -> 3 ;
 4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ;
 3 -> 4 ;
-5 [label="worst compactness <= 0.085\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ;
+5 [label="mean texture <= 18.935\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ;
 3 -> 5 ;
 6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ;
 5 -> 6 ;
@@ -34,7 +34,7 @@ edge [fontname="helvetica"] ;
 14 -> 15 ;
 16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ;
 15 -> 16 ;
-17 [label="smoothness error <= 0.005\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ;
+17 [label="mean concavity <= 0.07\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ;
 15 -> 17 ;
 18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
 17 -> 18 ;
diff --git a/doc/pub/week44/ipynb/Datafiles/cancer.png b/doc/pub/week44/ipynb/Datafiles/cancer.png
index 1edd37c90..d8075f573 100644
Binary files a/doc/pub/week44/ipynb/Datafiles/cancer.png and b/doc/pub/week44/ipynb/Datafiles/cancer.png differ
diff --git a/doc/pub/week44/ipynb/week44.ipynb b/doc/pub/week44/ipynb/week44.ipynb
index 9b0aca0ae..4990a769e 100644
--- a/doc/pub/week44/ipynb/week44.ipynb
+++ b/doc/pub/week44/ipynb/week44.ipynb
@@ -3,9 +3,7 @@
   {
    "cell_type": "markdown",
    "id": "d71885d1",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
     "doconce format html week44.do.txt --no_mako -->\n",
@@ -15,9 +13,7 @@
   {
    "cell_type": "markdown",
    "id": "51c93be7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# Week 44: Decision Trees, Ensemble methods  and Random Forests\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",
@@ -30,9 +26,7 @@
   {
    "cell_type": "markdown",
    "id": "dc2cab51",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Overview of week 44\n",
     "\n",
@@ -54,9 +48,7 @@
   {
    "cell_type": "markdown",
    "id": "2bf703ef",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Digression First\n",
     "\n",
@@ -72,9 +64,7 @@
   {
    "cell_type": "markdown",
    "id": "98778a6c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Decision trees, overarching aims\n",
     "\n",
@@ -103,9 +93,7 @@
   {
    "cell_type": "markdown",
    "id": "03ad6396",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Basics of a tree\n",
     "\n",
@@ -123,9 +111,7 @@
   {
    "cell_type": "markdown",
    "id": "d56e4131",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A Sketch of a Tree, Regression problem\n",
     "\n",
@@ -137,9 +123,7 @@
   {
    "cell_type": "markdown",
    "id": "15e9ccca",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A Sketch of a Tree, Classification  problem\n",
     "\n",
@@ -150,9 +134,7 @@
   {
    "cell_type": "markdown",
    "id": "1d3d956e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A typical Decision Tree with its pertinent Jargon, Classification Problem\n",
     "\n",
@@ -168,9 +150,7 @@
   {
    "cell_type": "markdown",
    "id": "26b7124c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## General Features\n",
     "\n",
@@ -191,9 +171,7 @@
   {
    "cell_type": "markdown",
    "id": "63bd32f3",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## How do we set it up?\n",
     "\n",
@@ -214,9 +192,7 @@
   {
    "cell_type": "markdown",
    "id": "e07dde87",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Decision trees and Regression"
    ]
@@ -225,11 +201,39 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "3b672587",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2nd degree coefficients:\n",
+      "zero power:  5.5512545666537125\n",
+      "first power:  0.05202946060040184\n",
+      "second power:  -0.000187081702984667\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "\n",
@@ -326,9 +330,7 @@
   {
    "cell_type": "markdown",
    "id": "71707f31",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Building a tree, regression\n",
     "\n",
@@ -348,9 +350,7 @@
   {
    "cell_type": "markdown",
    "id": "b9d1aa76",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\sum_{j=1}^J\\sum_{i\\in R_j}(y_i-\\overline{y}_{R_j})^2,\n",
@@ -360,9 +360,7 @@
   {
    "cell_type": "markdown",
    "id": "9bd688f8",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $\\overline{y}_{R_j}$  is the mean response for the training observations \n",
     "within box $j$."
@@ -371,9 +369,7 @@
   {
    "cell_type": "markdown",
    "id": "c75f76be",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A top-down approach, recursive binary splitting\n",
     "\n",
@@ -393,9 +389,7 @@
   {
    "cell_type": "markdown",
    "id": "69684b47",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Making a tree\n",
     "\n",
@@ -406,9 +400,7 @@
   {
    "cell_type": "markdown",
    "id": "55e24148",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\left\\{X\\vert x_j < s\\right\\},\n",
@@ -418,9 +410,7 @@
   {
    "cell_type": "markdown",
    "id": "337c3963",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and"
    ]
@@ -428,9 +418,7 @@
   {
    "cell_type": "markdown",
    "id": "1ce2e95c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\left\\{X\\vert x_j \\geq s\\right\\},\n",
@@ -440,9 +428,7 @@
   {
    "cell_type": "markdown",
    "id": "6b43c87d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "so that we obtain the lowest MSE, that is"
    ]
@@ -450,9 +436,7 @@
   {
    "cell_type": "markdown",
    "id": "5a752b5a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\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",
@@ -462,9 +446,7 @@
   {
    "cell_type": "markdown",
    "id": "02d8d908",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "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",
@@ -495,9 +477,7 @@
   {
    "cell_type": "markdown",
    "id": "bf3546d3",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Pruning the tree\n",
     "\n",
@@ -519,9 +499,7 @@
   {
    "cell_type": "markdown",
    "id": "56d9f28b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Cost complexity pruning\n",
     "\n",
@@ -531,9 +509,7 @@
   {
    "cell_type": "markdown",
    "id": "bb5a288c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\sum_{m=1}^{\\overline{T}}\\sum_{i:x_i\\in R_m}(y_i-\\overline{y}_{R_m})^2+\\alpha\\overline{T},\n",
@@ -543,9 +519,7 @@
   {
    "cell_type": "markdown",
    "id": "074d2165",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "is as small as possible. Here $\\overline{T}$ is \n",
     "the number of terminal nodes of the tree $T$ , $R_m$ is the\n",
@@ -571,9 +545,7 @@
   {
    "cell_type": "markdown",
    "id": "16c88686",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Schematic Regression Procedure\n",
     "\n",
@@ -597,9 +569,7 @@
   {
    "cell_type": "markdown",
    "id": "8025e3a5",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A Classification Tree\n",
     "\n",
@@ -620,9 +590,7 @@
   {
    "cell_type": "markdown",
    "id": "edc69600",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Growing a classification tree\n",
     "\n",
@@ -647,9 +615,7 @@
   {
    "cell_type": "markdown",
    "id": "b34390f9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Classification tree, how to split nodes\n",
     "\n",
@@ -666,9 +632,7 @@
   {
    "cell_type": "markdown",
    "id": "72bb5da6",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i=k).\n",
@@ -678,9 +642,7 @@
   {
    "cell_type": "markdown",
    "id": "1bea46b9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "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",
@@ -691,9 +653,7 @@
   {
    "cell_type": "markdown",
    "id": "83b2f14e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i\\ne k) = 1-p_{mk}.\n",
@@ -703,9 +663,7 @@
   {
    "cell_type": "markdown",
    "id": "93c3becf",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "* Gini index $g$"
    ]
@@ -713,9 +671,7 @@
   {
    "cell_type": "markdown",
    "id": "e34f4ce3",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "g = \\sum_{k=1}^K p_{mk}(1-p_{mk}).\n",
@@ -725,9 +681,7 @@
   {
    "cell_type": "markdown",
    "id": "6d09bc7d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "* Information entropy or just entropy $s$"
    ]
@@ -735,9 +689,7 @@
   {
    "cell_type": "markdown",
    "id": "f4d1aaeb",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "s = -\\sum_{k=1}^K p_{mk}\\log{p_{mk}}.\n",
@@ -747,9 +699,7 @@
   {
    "cell_type": "markdown",
    "id": "47632237",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Visualizing the Tree, Classification"
    ]
@@ -758,11 +708,118 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "85348704",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "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": [
+       "0"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import os\n",
     "from sklearn.datasets import load_breast_cancer\n",
@@ -802,9 +859,7 @@
   {
    "cell_type": "markdown",
    "id": "16feb17e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Visualizing the Tree, The Moons"
    ]
@@ -813,11 +868,19 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "bf5f4030",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Common imports\n",
     "import numpy as np\n",
@@ -848,9 +911,7 @@
   {
    "cell_type": "markdown",
    "id": "db30e9db",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Other ways of visualizing the trees\n",
     "\n",
@@ -861,11 +922,45 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "d62f4144",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 0.9166666666666666, 'X[2] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]'),\n",
+       " Text(0.4230769230769231, 0.75, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]'),\n",
+       " Text(0.5769230769230769, 0.75, 'X[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]'),\n",
+       " Text(0.3076923076923077, 0.5833333333333334, 'X[2] <= 4.95\\ngini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]'),\n",
+       " Text(0.15384615384615385, 0.4166666666666667, 'X[3] <= 1.65\\ngini = 0.041\\nsamples = 48\\nvalue = [0, 47, 1]'),\n",
+       " Text(0.07692307692307693, 0.25, 'gini = 0.0\\nsamples = 47\\nvalue = [0, 47, 0]'),\n",
+       " Text(0.23076923076923078, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
+       " Text(0.46153846153846156, 0.4166666666666667, 'X[3] <= 1.55\\ngini = 0.444\\nsamples = 6\\nvalue = [0, 2, 4]'),\n",
+       " Text(0.38461538461538464, 0.25, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
+       " Text(0.5384615384615384, 0.25, 'X[2] <= 5.45\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 2, 1]'),\n",
+       " Text(0.46153846153846156, 0.08333333333333333, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
+       " Text(0.6153846153846154, 0.08333333333333333, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
+       " Text(0.8461538461538461, 0.5833333333333334, 'X[2] <= 4.85\\ngini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]'),\n",
+       " Text(0.7692307692307693, 0.4166666666666667, 'X[1] <= 3.1\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
+       " Text(0.6923076923076923, 0.25, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]'),\n",
+       " Text(0.8461538461538461, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
+       " Text(0.9230769230769231, 0.4166666666666667, 'gini = 0.0\\nsamples = 43\\nvalue = [0, 0, 43]')]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "from sklearn import tree\n",
@@ -879,9 +974,7 @@
   {
    "cell_type": "markdown",
    "id": "66ef59a0",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Printing out as text\n",
     "\n",
@@ -893,11 +986,23 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "eb003507",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "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"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
@@ -912,9 +1017,7 @@
   {
    "cell_type": "markdown",
    "id": "7e23af78",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Algorithms for Setting up Decision Trees\n",
     "\n",
@@ -932,9 +1035,7 @@
   {
    "cell_type": "markdown",
    "id": "d45f60f4",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The CART algorithm for Classification\n",
     "\n",
@@ -949,9 +1050,7 @@
   {
    "cell_type": "markdown",
    "id": "2b82a6ee",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}G_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}G_{\\mathrm{right}},\n",
@@ -961,9 +1060,7 @@
   {
    "cell_type": "markdown",
    "id": "ded4f78f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "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",
@@ -978,9 +1075,7 @@
   {
    "cell_type": "markdown",
    "id": "e45062ff",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The CART algorithm for Regression\n",
     "\n",
@@ -991,9 +1086,7 @@
   {
    "cell_type": "markdown",
    "id": "c4eb8f40",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}\\mathrm{MSE}_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}\\mathrm{MSE}_{\\mathrm{right}}.\n",
@@ -1003,9 +1096,7 @@
   {
    "cell_type": "markdown",
    "id": "d04a8b2b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Here the MSE for a specific node is defined as"
    ]
@@ -1013,9 +1104,7 @@
   {
    "cell_type": "markdown",
    "id": "c2c78ab6",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\mathrm{MSE}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}(\\overline{y}_{\\mathrm{node}}-y_i)^2,\n",
@@ -1025,9 +1114,7 @@
   {
    "cell_type": "markdown",
    "id": "5330ffab",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "with"
    ]
@@ -1035,9 +1122,7 @@
   {
    "cell_type": "markdown",
    "id": "44796ffa",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\overline{y}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}y_i,\n",
@@ -1047,9 +1132,7 @@
   {
    "cell_type": "markdown",
    "id": "3336c40e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "the mean value of all observations in a specific node.\n",
     "\n",
@@ -1060,9 +1143,7 @@
   {
    "cell_type": "markdown",
    "id": "ce17050d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Why binary splits?\n",
     "\n",
@@ -1075,9 +1156,7 @@
   {
    "cell_type": "markdown",
    "id": "8f368dbe",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Computing a Tree using the Gini Index\n",
     "\n",
@@ -1101,9 +1180,7 @@
   {
    "cell_type": "markdown",
    "id": "66ce0b1b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The Table\n",
     "\n",
@@ -1129,9 +1206,7 @@
   {
    "cell_type": "markdown",
    "id": "f363c6b9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Computing the various Gini Indices\n",
     "\n",
@@ -1146,9 +1221,7 @@
   {
    "cell_type": "markdown",
    "id": "5017e81e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Computing the various Gini Indices, Hours slept\n",
     "\n",
@@ -1160,9 +1233,7 @@
   {
    "cell_type": "markdown",
    "id": "23c27590",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Computing the various Gini Indices, Hours studied\n",
     "\n",
@@ -1176,22 +1247,164 @@
   {
    "cell_type": "markdown",
    "id": "0238bc31",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A possible code using Scikit-Learn"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "6d3ad401",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "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>Trend</th>\n",
+       "      <th>Sleep</th>\n",
+       "      <th>Studied</th>\n",
+       "      <th>Grade</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Trend  Sleep  Studied  Grade\n",
+       "0      1      0        1      1\n",
+       "1      0      1        0      0\n",
+       "2      1      0        1      1\n",
+       "3      1      1        1      1\n",
+       "4      0      0        1      0\n",
+       "5      1      0        0      0\n",
+       "6      0      1        1      0\n",
+       "7      0      0        1      0\n",
+       "8      1      0        0      0\n",
+       "9      1      1        1      1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 0 1]\n",
+      " [0 1 0]\n",
+      " [1 0 1]\n",
+      " [1 1 1]\n",
+      " [0 0 1]\n",
+      " [1 0 0]\n",
+      " [0 1 1]\n",
+      " [0 0 1]\n",
+      " [1 0 0]\n",
+      " [1 1 1]]\n",
+      "Train set accuracy with Decision Tree: 1.00\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Common imports\n",
     "import numpy as np\n",
@@ -1258,19 +1471,13 @@
     "    filled=True\n",
     ")\n",
     "cmd = 'dot -Tpng DataFiles/grade.dot -o DataFiles/grades.png'\n",
-    "os.system(cmd)\n",
-    "\n",
-    "\n",
-    "#data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])\n",
-    "#df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']"
+    "os.system(cmd)\n"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "5bf4e701",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Further example: Computing the Gini index\n",
     "\n",
@@ -1312,9 +1519,7 @@
   {
    "cell_type": "markdown",
    "id": "b161aa79",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Simple Python Code to read in Data and perform Classification"
    ]
@@ -1323,10 +1528,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "078a139d",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Common imports\n",
@@ -1401,9 +1603,7 @@
   {
    "cell_type": "markdown",
    "id": "e47d975e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Computing the Gini Factor\n",
     "\n",
@@ -1419,10 +1619,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "47e6cb0e",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Split a dataset based on an attribute and an attribute value\n",
@@ -1490,9 +1687,7 @@
   {
    "cell_type": "markdown",
    "id": "277cae80",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Another example, the moons"
    ]
@@ -1501,10 +1696,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "34a3f693",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from __future__ import division, print_function, unicode_literals\n",
@@ -1576,9 +1768,7 @@
   {
    "cell_type": "markdown",
    "id": "ad6c5f89",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Playing around with regions"
    ]
@@ -1587,10 +1777,7 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "89770dc2",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.random.seed(6)\n",
@@ -1618,9 +1805,7 @@
   {
    "cell_type": "markdown",
    "id": "2d2ec3db",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Regression trees"
    ]
@@ -1629,10 +1814,7 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "72026bb5",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Quadratic training set + noise\n",
@@ -1647,10 +1829,7 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "38fbe6f2",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.tree import DecisionTreeRegressor\n",
@@ -1662,9 +1841,7 @@
   {
    "cell_type": "markdown",
    "id": "cc4785b1",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Final regressor code"
    ]
@@ -1673,10 +1850,7 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "54b15ed3",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.tree import DecisionTreeRegressor\n",
@@ -1723,10 +1897,7 @@
    "cell_type": "code",
    "execution_count": 14,
    "id": "92e9f134",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
@@ -1762,9 +1933,7 @@
   {
    "cell_type": "markdown",
    "id": "d6dfb6d9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Pros and cons of trees, pros\n",
     "\n",
@@ -1786,9 +1955,7 @@
   {
    "cell_type": "markdown",
    "id": "6683775c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Disadvantages\n",
     "\n",
@@ -1814,9 +1981,7 @@
   {
    "cell_type": "markdown",
    "id": "bf52cfd7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods\n",
     "\n",
@@ -1845,9 +2010,7 @@
   {
    "cell_type": "markdown",
    "id": "2c39236d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## An Overview of Ensemble Methods\n",
     "\n",
@@ -1861,9 +2024,7 @@
   {
    "cell_type": "markdown",
    "id": "437f82d0",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Why Voting?\n",
     "\n",
@@ -1885,9 +2046,7 @@
   {
    "cell_type": "markdown",
    "id": "12c69836",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Tossing coins\n",
     "\n",
@@ -1916,9 +2075,7 @@
   {
    "cell_type": "markdown",
    "id": "a8614384",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Standard imports first"
    ]
@@ -1927,10 +2084,7 @@
    "cell_type": "code",
    "execution_count": 15,
    "id": "60dd5ef6",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Common imports\n",
@@ -1975,9 +2129,7 @@
   {
    "cell_type": "markdown",
    "id": "0323fdfd",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Simple Voting Example, head or tail"
    ]
@@ -1986,10 +2138,7 @@
    "cell_type": "code",
    "execution_count": 16,
    "id": "74dfd159",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "\n",
@@ -2020,9 +2169,7 @@
   {
    "cell_type": "markdown",
    "id": "3f8db29e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Using the Voting Classifier\n",
     "\n",
@@ -2033,10 +2180,7 @@
    "cell_type": "code",
    "execution_count": 17,
    "id": "1f4c6479",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
@@ -2086,9 +2230,7 @@
   {
    "cell_type": "markdown",
    "id": "7b578c34",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Voting and Bagging"
    ]
@@ -2097,10 +2239,7 @@
    "cell_type": "code",
    "execution_count": 18,
    "id": "891c848b",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
@@ -2127,10 +2266,7 @@
    "cell_type": "code",
    "execution_count": 19,
    "id": "47b632ab",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
@@ -2145,10 +2281,7 @@
    "cell_type": "code",
    "execution_count": 20,
    "id": "e2f5031e",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "log_clf = LogisticRegression(random_state=42)\n",
@@ -2165,10 +2298,7 @@
    "cell_type": "code",
    "execution_count": 21,
    "id": "b0719293",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
@@ -2182,9 +2312,7 @@
   {
    "cell_type": "markdown",
    "id": "b9966fed",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Bagging\n",
     "\n",
@@ -2204,9 +2332,7 @@
   {
    "cell_type": "markdown",
    "id": "e8613549",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## More bagging\n",
     "\n",
@@ -2236,9 +2362,7 @@
   {
    "cell_type": "markdown",
    "id": "72be9bd5",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Making your own Bootstrap: Changing the Level of the Decision Tree\n",
     "\n",
@@ -2250,10 +2374,7 @@
    "cell_type": "code",
    "execution_count": 22,
    "id": "b2f5c535",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "\n",
@@ -2319,9 +2440,7 @@
   {
    "cell_type": "markdown",
    "id": "decc7681",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Random forests\n",
     "\n",
@@ -2342,9 +2461,7 @@
   {
    "cell_type": "markdown",
    "id": "b047a99c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "m\\approx \\sqrt{p}.\n",
@@ -2354,9 +2471,7 @@
   {
    "cell_type": "markdown",
    "id": "cfb22949",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In building a random forest, at\n",
     "each split in the tree, the algorithm is not even allowed to consider\n",
@@ -2379,9 +2494,7 @@
   {
    "cell_type": "markdown",
    "id": "8bec594f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Random Forest Algorithm\n",
     "The algorithm described here can be applied to both classification and regression problems.\n",
@@ -2405,9 +2518,7 @@
   {
    "cell_type": "markdown",
    "id": "23480abb",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Random Forests Compared with other Methods on the Cancer Data"
    ]
@@ -2416,10 +2527,7 @@
    "cell_type": "code",
    "execution_count": 23,
    "id": "c7542bff",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -2488,9 +2596,7 @@
   {
    "cell_type": "markdown",
    "id": "e4bead63",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Recall that the cumulative gains curve shows the percentage of the\n",
     "overall number of cases in a given category *gained* by targeting a\n",
@@ -2504,9 +2610,7 @@
   {
    "cell_type": "markdown",
    "id": "78db991e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Compare  Bagging on Trees with Random Forests"
    ]
@@ -2515,10 +2619,7 @@
    "cell_type": "code",
    "execution_count": 24,
    "id": "c12e0ecd",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "bag_clf = BaggingClassifier(\n",
@@ -2530,10 +2631,7 @@
    "cell_type": "code",
    "execution_count": 25,
    "id": "9b3dadd3",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "bag_clf.fit(X_train, y_train)\n",
@@ -2546,7 +2644,25 @@
    ]
   }
  ],
- "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",
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+   "pygments_lexer": "ipython3",
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+ },
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  "nbformat_minor": 5
 }