diff --git a/doc/LectureNotes/DataFiles/cancer.dot b/doc/LectureNotes/DataFiles/cancer.dot index 5a2b6cf12..23d4f0431 100644 --- a/doc/LectureNotes/DataFiles/cancer.dot +++ b/doc/LectureNotes/DataFiles/cancer.dot @@ -6,23 +6,23 @@ edge [fontname="helvetica"] ; 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; 2 [label="worst concave points <= 0.135\ngini = 0.031\nsamples = 253\nvalue = [[249, 4]\n[4, 249]]", fillcolor="#e78946"] ; 1 -> 2 ; -3 [label="radius error <= 0.643\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ; +3 [label="area error <= 48.975\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ; 2 -> 3 ; 4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ; 3 -> 4 ; -5 [label="worst perimeter <= 87.07\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; +5 [label="mean perimeter <= 78.51\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 ; 7 [label="gini = 0.0\nsamples = 2\nvalue = [[2, 0]\n[0, 2]]", fillcolor="#e58139"] ; 5 -> 7 ; -8 [label="worst texture <= 29.455\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#fae9dd"] ; +8 [label="mean texture <= 20.84\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#fae9dd"] ; 2 -> 8 ; 9 [label="gini = 0.0\nsamples = 8\nvalue = [[8, 0]\n[0, 8]]", fillcolor="#e58139"] ; 8 -> 9 ; 10 [label="gini = 0.0\nsamples = 3\nvalue = [[0, 3]\n[3, 0]]", fillcolor="#e58139"] ; 8 -> 10 ; -11 [label="mean texture <= 16.22\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ; +11 [label="area error <= 13.475\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ; 1 -> 11 ; 12 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 11 -> 12 ; @@ -30,11 +30,11 @@ edge [fontname="helvetica"] ; 11 -> 13 ; 14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#f0b68c"] ; 0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; -15 [label="worst concavity <= 0.318\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ; +15 [label="worst perimeter <= 116.8\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ; 14 -> 15 ; 16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ; 15 -> 16 ; -17 [label="mean radius <= 15.06\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; +17 [label="smoothness error <= 0.005\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 ; @@ -48,7 +48,7 @@ edge [fontname="helvetica"] ; 21 -> 22 ; 23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139"] ; 21 -> 23 ; -24 [label="worst smoothness <= 0.096\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ; +24 [label="mean smoothness <= 0.079\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ; 20 -> 24 ; 25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 24 -> 25 ; diff --git a/doc/LectureNotes/DataFiles/cancer.png b/doc/LectureNotes/DataFiles/cancer.png index b85bd6310..8e16737a9 100644 Binary files a/doc/LectureNotes/DataFiles/cancer.png and b/doc/LectureNotes/DataFiles/cancer.png differ diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle index 2b37c5929..65f06bb8b 100644 Binary files a/doc/LectureNotes/_build/.doctrees/environment.pickle and b/doc/LectureNotes/_build/.doctrees/environment.pickle differ diff --git a/doc/LectureNotes/_build/.doctrees/week46.doctree b/doc/LectureNotes/_build/.doctrees/week46.doctree index 0d9d5e0bb..e8f8e3ecc 100644 Binary files a/doc/LectureNotes/_build/.doctrees/week46.doctree and b/doc/LectureNotes/_build/.doctrees/week46.doctree differ diff --git a/doc/LectureNotes/_build/html/_images/week46_9_1.png b/doc/LectureNotes/_build/html/_images/week46_9_1.png index 7f50abbd5..1e08215a0 100644 Binary files a/doc/LectureNotes/_build/html/_images/week46_9_1.png and b/doc/LectureNotes/_build/html/_images/week46_9_1.png differ diff --git a/doc/LectureNotes/_build/html/_images/week46_9_2.png b/doc/LectureNotes/_build/html/_images/week46_9_2.png index b037f509f..3cf74c53c 100644 Binary files a/doc/LectureNotes/_build/html/_images/week46_9_2.png and b/doc/LectureNotes/_build/html/_images/week46_9_2.png differ diff --git a/doc/LectureNotes/_build/html/_sources/week46.ipynb b/doc/LectureNotes/_build/html/_sources/week46.ipynb index 0b23538ed..87bc2f83d 100644 --- a/doc/LectureNotes/_build/html/_sources/week46.ipynb +++ b/doc/LectureNotes/_build/html/_sources/week46.ipynb @@ -2,8 +2,10 @@ "cells": [ { "cell_type": "markdown", - "id": "f4d3b2c9", - "metadata": {}, + "id": "f9935da5", + "metadata": { + "editable": true + }, "source": [ "\n", @@ -12,8 +14,10 @@ }, { "cell_type": "markdown", - "id": "57cda95f", - "metadata": {}, + "id": "0b990b1c", + "metadata": { + "editable": true + }, "source": [ "# Week 46: 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", @@ -23,8 +27,10 @@ }, { "cell_type": "markdown", - "id": "0ab525ed", - "metadata": {}, + "id": "71e9f143", + "metadata": { + "editable": true + }, "source": [ "## Plan for week 46\n", "\n", @@ -44,7 +50,9 @@ "\n", " * These lecture notes\n", "\n", - " * [Video of lecture to be added](https://youtu.be/)\n", + " * [Video of lecture](https://youtu.be/PMswUwhYa7k)\n", + "\n", + " * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov16.pdf) \n", "\n", " * [Video on Decision trees](https://www.youtube.com/watch?v=RmajweUFKvM&ab_channel=Simplilearn)\n", "\n", @@ -53,8 +61,10 @@ }, { "cell_type": "markdown", - "id": "3c8e0d42", - "metadata": {}, + "id": "00b82c38", + "metadata": { + "editable": true + }, "source": [ "## Decision trees, overarching aims\n", "\n", @@ -82,8 +92,10 @@ }, { "cell_type": "markdown", - "id": "893c9b6f", - "metadata": {}, + "id": "c733808f", + "metadata": { + "editable": true + }, "source": [ "## Basics of a tree\n", "\n", @@ -100,8 +112,10 @@ }, { "cell_type": "markdown", - "id": "89b0fc63", - "metadata": {}, + "id": "2b60a24e", + "metadata": { + "editable": true + }, "source": [ "## A typical Decision Tree with its pertinent Jargon, Classification Problem\n", "\n", @@ -116,8 +130,10 @@ }, { "cell_type": "markdown", - "id": "d4354730", - "metadata": {}, + "id": "1a8dc5f7", + "metadata": { + "editable": true + }, "source": [ "## General Features\n", "\n", @@ -137,8 +153,10 @@ }, { "cell_type": "markdown", - "id": "9db7330a", - "metadata": {}, + "id": "188465ed", + "metadata": { + "editable": true + }, "source": [ "## How do we set it up?\n", "\n", @@ -158,8 +176,10 @@ }, { "cell_type": "markdown", - "id": "331ebf1d", - "metadata": {}, + "id": "7f927d1c", + "metadata": { + "editable": true + }, "source": [ "## Decision trees and Regression" ] @@ -167,40 +187,12 @@ { "cell_type": "code", "execution_count": 1, - "id": "122986df", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2nd degree coefficients:\n", - "zero power: 0.7163225806451621\n", - "first power: 0.17047319577389736\n", - "second power: -0.0006648714674365666\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "id": "92377283", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "%matplotlib inline\n", "\n", @@ -296,8 +288,10 @@ }, { "cell_type": "markdown", - "id": "967f86f4", - "metadata": {}, + "id": "9defc5e7", + "metadata": { + "editable": true + }, "source": [ "## Building a tree, regression\n", "\n", @@ -316,8 +310,10 @@ }, { "cell_type": "markdown", - "id": "58c89f85", - "metadata": {}, + "id": "97bfeb0f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\sum_{j=1}^J\\sum_{i\\in R_j}(y_i-\\overline{y}_{R_j})^2,\n", @@ -326,8 +322,10 @@ }, { "cell_type": "markdown", - "id": "8d28defb", - "metadata": {}, + "id": "60e3b6f5", + "metadata": { + "editable": true + }, "source": [ "where $\\overline{y}_{R_j}$ is the mean response for the training observations \n", "within box $j$." @@ -335,8 +333,10 @@ }, { "cell_type": "markdown", - "id": "3f6c36d6", - "metadata": {}, + "id": "6a950a77", + "metadata": { + "editable": true + }, "source": [ "## A top-down approach, recursive binary splitting\n", "\n", @@ -355,8 +355,10 @@ }, { "cell_type": "markdown", - "id": "a89a52db", - "metadata": {}, + "id": "b7aa91ac", + "metadata": { + "editable": true + }, "source": [ "## Making a tree\n", "\n", @@ -366,8 +368,10 @@ }, { "cell_type": "markdown", - "id": "5feb6c75", - "metadata": {}, + "id": "ecfe94f2", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\left\\{X\\vert x_j < s\\right\\},\n", @@ -376,16 +380,20 @@ }, { "cell_type": "markdown", - "id": "8de30cae", - "metadata": {}, + "id": "b4970af3", + "metadata": { + "editable": true + }, "source": [ "and" ] }, { "cell_type": "markdown", - "id": "95d5c167", - "metadata": {}, + "id": "85bd3a40", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\left\\{X\\vert x_j \\geq s\\right\\},\n", @@ -394,16 +402,20 @@ }, { "cell_type": "markdown", - "id": "c5f10599", - "metadata": {}, + "id": "1ea5b535", + "metadata": { + "editable": true + }, "source": [ "so that we obtain the lowest MSE, that is" ] }, { "cell_type": "markdown", - "id": "ff6f03cb", - "metadata": {}, + "id": "b5672b44", + "metadata": { + "editable": true + }, "source": [ "$$\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", @@ -412,8 +424,10 @@ }, { "cell_type": "markdown", - "id": "9f031abb", - "metadata": {}, + "id": "999be0b7", + "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", @@ -443,8 +457,10 @@ }, { "cell_type": "markdown", - "id": "83f9c272", - "metadata": {}, + "id": "ac6a5d56", + "metadata": { + "editable": true + }, "source": [ "## Pruning the tree\n", "\n", @@ -465,8 +481,10 @@ }, { "cell_type": "markdown", - "id": "9f23f5ac", - "metadata": {}, + "id": "63deb833", + "metadata": { + "editable": true + }, "source": [ "## Cost complexity pruning\n", "\n", @@ -475,8 +493,10 @@ }, { "cell_type": "markdown", - "id": "a9b2646e", - "metadata": {}, + "id": "9f4c860f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\sum_{m=1}^{\\overline{T}}\\sum_{i:x_i\\in R_m}(y_i-\\overline{y}_{R_m})^2+\\alpha\\overline{T},\n", @@ -485,8 +505,10 @@ }, { "cell_type": "markdown", - "id": "8c9f1038", - "metadata": {}, + "id": "fd542b6e", + "metadata": { + "editable": true + }, "source": [ "is as small as possible. Here $\\overline{T}$ is \n", "the number of terminal nodes of the tree $T$ , $R_m$ is the\n", @@ -511,8 +533,10 @@ }, { "cell_type": "markdown", - "id": "1e4cf9ca", - "metadata": {}, + "id": "c5099f78", + "metadata": { + "editable": true + }, "source": [ "## Schematic Regression Procedure\n", "\n", @@ -535,8 +559,10 @@ }, { "cell_type": "markdown", - "id": "328198af", - "metadata": {}, + "id": "88e2ce13", + "metadata": { + "editable": true + }, "source": [ "## A Classification Tree\n", "\n", @@ -556,8 +582,10 @@ }, { "cell_type": "markdown", - "id": "338052bc", - "metadata": {}, + "id": "9fab855d", + "metadata": { + "editable": true + }, "source": [ "## Growing a classification tree\n", "\n", @@ -581,8 +609,10 @@ }, { "cell_type": "markdown", - "id": "c96ca06b", - "metadata": {}, + "id": "8dc57784", + "metadata": { + "editable": true + }, "source": [ "## Classification tree, how to split nodes\n", "\n", @@ -598,8 +628,10 @@ }, { "cell_type": "markdown", - "id": "970d1672", - "metadata": {}, + "id": "3a23d427", + "metadata": { + "editable": true + }, "source": [ "$$\n", "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i=k).\n", @@ -608,8 +640,10 @@ }, { "cell_type": "markdown", - "id": "e5a9ac3c", - "metadata": {}, + "id": "78e66687", + "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", @@ -619,8 +653,10 @@ }, { "cell_type": "markdown", - "id": "a0e10726", - "metadata": {}, + "id": "9157055f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i\\ne k) = 1-p_{mk}.\n", @@ -629,16 +665,20 @@ }, { "cell_type": "markdown", - "id": "f73a5a66", - "metadata": {}, + "id": "259d56c7", + "metadata": { + "editable": true + }, "source": [ "* Gini index $g$" ] }, { "cell_type": "markdown", - "id": "c6e5ec5f", - "metadata": {}, + "id": "66f8ee1a", + "metadata": { + "editable": true + }, "source": [ "$$\n", "g = \\sum_{k=1}^K p_{mk}(1-p_{mk}).\n", @@ -647,16 +687,20 @@ }, { "cell_type": "markdown", - "id": "c50e4c55", - "metadata": {}, + "id": "7478d6d0", + "metadata": { + "editable": true + }, "source": [ "* Information entropy or just entropy $s$" ] }, { "cell_type": "markdown", - "id": "34f4deed", - "metadata": {}, + "id": "30737ebf", + "metadata": { + "editable": true + }, "source": [ "$$\n", "s = -\\sum_{k=1}^K p_{mk}\\log{p_{mk}}.\n", @@ -665,8 +709,10 @@ }, { "cell_type": "markdown", - "id": "b2f791b8", - "metadata": {}, + "id": "c145a6a4", + "metadata": { + "editable": true + }, "source": [ "## Visualizing the Tree, Classification" ] @@ -674,119 +720,12 @@ { "cell_type": "code", "execution_count": 2, - "id": "b373a31c", - "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" - } - ], + "id": "c04064d0", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "import os\n", "from sklearn.datasets import load_breast_cancer\n", @@ -825,8 +764,10 @@ }, { "cell_type": "markdown", - "id": "f1649fd9", - "metadata": {}, + "id": "da6433b2", + "metadata": { + "editable": true + }, "source": [ "## Visualizing the Tree, The Moons" ] @@ -834,20 +775,12 @@ { "cell_type": "code", "execution_count": 3, - "id": "63e625c0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "id": "7a884220", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "# Common imports\n", "import numpy as np\n", @@ -877,8 +810,10 @@ }, { "cell_type": "markdown", - "id": "119ea0ae", - "metadata": {}, + "id": "4dfa7512", + "metadata": { + "editable": true + }, "source": [ "## Other ways of visualizing the trees\n", "\n", @@ -888,46 +823,12 @@ { "cell_type": "code", "execution_count": 4, - "id": "711bdf8d", - "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] <= 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "id": "bc172ee1", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "from sklearn.datasets import load_iris\n", "from sklearn import tree\n", @@ -940,8 +841,10 @@ }, { "cell_type": "markdown", - "id": "a80a7f27", - "metadata": {}, + "id": "b7996538", + "metadata": { + "editable": true + }, "source": [ "## Printing out as text\n", "\n", @@ -952,24 +855,12 @@ { "cell_type": "code", "execution_count": 5, - "id": "3aa4b27b", - "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" - ] - } - ], + "id": "18c69627", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "from sklearn.datasets import load_iris\n", "from sklearn.tree import DecisionTreeClassifier\n", @@ -983,8 +874,10 @@ }, { "cell_type": "markdown", - "id": "e155d65a", - "metadata": {}, + "id": "1d734081", + "metadata": { + "editable": true + }, "source": [ "## Algorithms for Setting up Decision Trees\n", "\n", @@ -1001,8 +894,10 @@ }, { "cell_type": "markdown", - "id": "9f64d255", - "metadata": {}, + "id": "17e0e2f9", + "metadata": { + "editable": true + }, "source": [ "## The CART algorithm for Classification\n", "\n", @@ -1016,8 +911,10 @@ }, { "cell_type": "markdown", - "id": "c67ea6bd", - "metadata": {}, + "id": "73fc933b", + "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", @@ -1026,8 +923,10 @@ }, { "cell_type": "markdown", - "id": "60be0c2f", - "metadata": {}, + "id": "f8ca9923", + "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", @@ -1041,8 +940,10 @@ }, { "cell_type": "markdown", - "id": "68adc691", - "metadata": {}, + "id": "feb6fee3", + "metadata": { + "editable": true + }, "source": [ "## The CART algorithm for Regression\n", "\n", @@ -1052,8 +953,10 @@ }, { "cell_type": "markdown", - "id": "3aa84faa", - "metadata": {}, + "id": "24b0aea3", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}\\mathrm{MSE}_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}\\mathrm{MSE}_{\\mathrm{right}}.\n", @@ -1062,16 +965,20 @@ }, { "cell_type": "markdown", - "id": "05821fe6", - "metadata": {}, + "id": "8a01610a", + "metadata": { + "editable": true + }, "source": [ "Here the MSE for a specific node is defined as" ] }, { "cell_type": "markdown", - "id": "321fb878", - "metadata": {}, + "id": "9c28e4ab", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{MSE}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}(\\overline{y}_{\\mathrm{node}}-y_i)^2,\n", @@ -1080,16 +987,20 @@ }, { "cell_type": "markdown", - "id": "5703ea44", - "metadata": {}, + "id": "76dc3641", + "metadata": { + "editable": true + }, "source": [ "with" ] }, { "cell_type": "markdown", - "id": "6b6cf145", - "metadata": {}, + "id": "60f57c10", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\overline{y}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}y_i,\n", @@ -1098,8 +1009,10 @@ }, { "cell_type": "markdown", - "id": "57f159ee", - "metadata": {}, + "id": "8c3e3601", + "metadata": { + "editable": true + }, "source": [ "the mean value of all observations in a specific node.\n", "\n", @@ -1109,8 +1022,10 @@ }, { "cell_type": "markdown", - "id": "8dba6c9f", - "metadata": {}, + "id": "0be42f06", + "metadata": { + "editable": true + }, "source": [ "## Why binary splits?\n", "\n", @@ -1122,8 +1037,10 @@ }, { "cell_type": "markdown", - "id": "54686dd2", - "metadata": {}, + "id": "42ed666b", + "metadata": { + "editable": true + }, "source": [ "## Computing a Tree using the Gini Index\n", "\n", @@ -1146,8 +1063,10 @@ }, { "cell_type": "markdown", - "id": "226714bc", - "metadata": {}, + "id": "4ab2f2b9", + "metadata": { + "editable": true + }, "source": [ "## The Table\n", "\n", @@ -1172,8 +1091,10 @@ }, { "cell_type": "markdown", - "id": "132a6df7", - "metadata": {}, + "id": "df7b209e", + "metadata": { + "editable": true + }, "source": [ "## Computing the various Gini Indices\n", "\n", @@ -1187,8 +1108,10 @@ }, { "cell_type": "markdown", - "id": "75ab3e53", - "metadata": {}, + "id": "0f77f79d", + "metadata": { + "editable": true + }, "source": [ "## Computing the various Gini Indices, Hours slept\n", "\n", @@ -1199,8 +1122,10 @@ }, { "cell_type": "markdown", - "id": "be9d82ec", - "metadata": {}, + "id": "737f4e14", + "metadata": { + "editable": true + }, "source": [ "## Computing the various Gini Indices, Hours studied\n", "\n", @@ -1213,165 +1138,23 @@ }, { "cell_type": "markdown", - "id": "b502bb89", - "metadata": {}, + "id": "e2c139d1", + "metadata": { + "editable": true + }, "source": [ "## A possible code using Scikit-Learn" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "2e5fc857", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Grade Trend Hours slept Hours 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" - } - ], + "execution_count": 6, + "id": "d0089709", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "# Common imports\n", "import numpy as np\n", @@ -1437,8 +1220,10 @@ }, { "cell_type": "markdown", - "id": "fe2aa246", - "metadata": {}, + "id": "5f185aef", + "metadata": { + "editable": true + }, "source": [ "## Further example: Computing the Gini index\n", "\n", @@ -1479,8 +1264,10 @@ }, { "cell_type": "markdown", - "id": "46f289da", - "metadata": {}, + "id": "fe73a991", + "metadata": { + "editable": true + }, "source": [ "## Simple Python Code to read in Data and perform Classification" ] @@ -1488,8 +1275,11 @@ { "cell_type": "code", "execution_count": 7, - "id": "38aedbca", - "metadata": {}, + "id": "9b082c47", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# Common imports\n", @@ -1563,8 +1353,10 @@ }, { "cell_type": "markdown", - "id": "a6f5da59", - "metadata": {}, + "id": "df9287bb", + "metadata": { + "editable": true + }, "source": [ "## Computing the Gini Factor\n", "\n", @@ -1579,8 +1371,11 @@ { "cell_type": "code", "execution_count": 8, - "id": "e51855f9", - "metadata": {}, + "id": "00d95a16", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# Split a dataset based on an attribute and an attribute value\n", @@ -1647,8 +1442,10 @@ }, { "cell_type": "markdown", - "id": "f6add3e5", - "metadata": {}, + "id": "b6452b51", + "metadata": { + "editable": true + }, "source": [ "## Regression trees" ] @@ -1656,8 +1453,11 @@ { "cell_type": "code", "execution_count": 9, - "id": "74ecc649", - "metadata": {}, + "id": "3a98b310", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# Quadratic training set + noise\n", @@ -1671,8 +1471,11 @@ { "cell_type": "code", "execution_count": 10, - "id": "04024d89", - "metadata": {}, + "id": "1f8e183f", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", @@ -1683,8 +1486,10 @@ }, { "cell_type": "markdown", - "id": "878b4d23", - "metadata": {}, + "id": "6c91981e", + "metadata": { + "editable": true + }, "source": [ "## Final regressor code" ] @@ -1692,8 +1497,11 @@ { "cell_type": "code", "execution_count": 11, - "id": "3c96bff5", - "metadata": {}, + "id": "c9b69f54", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", @@ -1739,8 +1547,11 @@ { "cell_type": "code", "execution_count": 12, - "id": "527b27ca", - "metadata": {}, + "id": "53db8f73", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "tree_reg1 = DecisionTreeRegressor(random_state=42)\n", @@ -1775,8 +1586,10 @@ }, { "cell_type": "markdown", - "id": "f2a0dd48", - "metadata": {}, + "id": "3be38ddf", + "metadata": { + "editable": true + }, "source": [ "## Pros and cons of trees, pros\n", "\n", @@ -1797,8 +1610,10 @@ }, { "cell_type": "markdown", - "id": "9f896560", - "metadata": {}, + "id": "e4aaab5f", + "metadata": { + "editable": true + }, "source": [ "## Disadvantages\n", "\n", @@ -1823,8 +1638,10 @@ }, { "cell_type": "markdown", - "id": "3f6f50e2", - "metadata": {}, + "id": "0010cb58", + "metadata": { + "editable": true + }, "source": [ "## Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods\n", "\n", @@ -1852,8 +1669,10 @@ }, { "cell_type": "markdown", - "id": "24509012", - "metadata": {}, + "id": "f3a71c11", + "metadata": { + "editable": true + }, "source": [ "## An Overview of Ensemble Methods\n", "\n", @@ -1866,8 +1685,10 @@ }, { "cell_type": "markdown", - "id": "15a871bc", - "metadata": {}, + "id": "aef5e772", + "metadata": { + "editable": true + }, "source": [ "## Why Voting?\n", "\n", @@ -1888,8 +1709,10 @@ }, { "cell_type": "markdown", - "id": "e6d75533", - "metadata": {}, + "id": "a30b02f8", + "metadata": { + "editable": true + }, "source": [ "## Tossing coins\n", "\n", @@ -1917,8 +1740,10 @@ }, { "cell_type": "markdown", - "id": "8ecb23d0", - "metadata": {}, + "id": "3afd02ed", + "metadata": { + "editable": true + }, "source": [ "## Standard imports first" ] @@ -1926,8 +1751,11 @@ { "cell_type": "code", "execution_count": 13, - "id": "b42d0a08", - "metadata": {}, + "id": "4a1b7a89", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# Common imports\n", @@ -1971,8 +1799,10 @@ }, { "cell_type": "markdown", - "id": "e3060cfd", - "metadata": {}, + "id": "0c32e96d", + "metadata": { + "editable": true + }, "source": [ "## Simple Voting Example, head or tail" ] @@ -1980,8 +1810,11 @@ { "cell_type": "code", "execution_count": 14, - "id": "59d25264", - "metadata": {}, + "id": "4d1c99f7", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "\n", @@ -2011,8 +1844,10 @@ }, { "cell_type": "markdown", - "id": "f8cf0e6e", - "metadata": {}, + "id": "e0e3f1dc", + "metadata": { + "editable": true + }, "source": [ "## Using the Voting Classifier\n", "\n", @@ -2022,8 +1857,11 @@ { "cell_type": "code", "execution_count": 15, - "id": "76fd4c2d", - "metadata": {}, + "id": "c3d00f7d", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", @@ -2072,8 +1910,10 @@ }, { "cell_type": "markdown", - "id": "eacefe6c", - "metadata": {}, + "id": "f0d12f2c", + "metadata": { + "editable": true + }, "source": [ "## Voting and Bagging" ] @@ -2081,8 +1921,11 @@ { "cell_type": "code", "execution_count": 16, - "id": "796dfa6b", - "metadata": {}, + "id": "1bbde2a9", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", @@ -2108,8 +1951,11 @@ { "cell_type": "code", "execution_count": 17, - "id": "90ec162f", - "metadata": {}, + "id": "80a82744", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", @@ -2123,8 +1969,11 @@ { "cell_type": "code", "execution_count": 18, - "id": "e46dcb77", - "metadata": {}, + "id": "65c732b4", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "log_clf = LogisticRegression(random_state=42)\n", @@ -2140,8 +1989,11 @@ { "cell_type": "code", "execution_count": 19, - "id": "67a3b080", - "metadata": {}, + "id": "956f7ec5", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", @@ -2154,8 +2006,10 @@ }, { "cell_type": "markdown", - "id": "f0d51672", - "metadata": {}, + "id": "ee527167", + "metadata": { + "editable": true + }, "source": [ "## Bagging\n", "\n", @@ -2174,8 +2028,10 @@ }, { "cell_type": "markdown", - "id": "ab182ea8", - "metadata": {}, + "id": "354baed9", + "metadata": { + "editable": true + }, "source": [ "## More bagging\n", "\n", @@ -2204,8 +2060,10 @@ }, { "cell_type": "markdown", - "id": "998512be", - "metadata": {}, + "id": "fc1a2451", + "metadata": { + "editable": true + }, "source": [ "## Making your own Bootstrap: Changing the Level of the Decision Tree\n", "\n", @@ -2216,8 +2074,11 @@ { "cell_type": "code", "execution_count": 20, - "id": "6ac20f8b", - "metadata": {}, + "id": "129bb9fb", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "\n", @@ -2282,8 +2143,10 @@ }, { "cell_type": "markdown", - "id": "c9a44ff4", - "metadata": {}, + "id": "170b00ab", + "metadata": { + "editable": true + }, "source": [ "## Random forests\n", "\n", @@ -2303,8 +2166,10 @@ }, { "cell_type": "markdown", - "id": "74f9056f", - "metadata": {}, + "id": "58a4aa65", + "metadata": { + "editable": true + }, "source": [ "$$\n", "m\\approx \\sqrt{p}.\n", @@ -2313,8 +2178,10 @@ }, { "cell_type": "markdown", - "id": "9a0166e1", - "metadata": {}, + "id": "a5ed07c1", + "metadata": { + "editable": true + }, "source": [ "In building a random forest, at\n", "each split in the tree, the algorithm is not even allowed to consider\n", @@ -2336,8 +2203,10 @@ }, { "cell_type": "markdown", - "id": "7e5dd3c9", - "metadata": {}, + "id": "c5369edb", + "metadata": { + "editable": true + }, "source": [ "## Random Forest Algorithm\n", "The algorithm described here can be applied to both classification and regression problems.\n", @@ -2360,8 +2229,10 @@ }, { "cell_type": "markdown", - "id": "b2476d94", - "metadata": {}, + "id": "4355c5ad", + "metadata": { + "editable": true + }, "source": [ "## Random Forests Compared with other Methods on the Cancer Data" ] @@ -2369,8 +2240,11 @@ { "cell_type": "code", "execution_count": 21, - "id": "0a56d5de", - "metadata": {}, + "id": "0fbcd18f", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -2438,8 +2312,10 @@ }, { "cell_type": "markdown", - "id": "ef32420e", - "metadata": {}, + "id": "4072de62", + "metadata": { + "editable": true + }, "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", @@ -2452,8 +2328,10 @@ }, { "cell_type": "markdown", - "id": "5820ebfd", - "metadata": {}, + "id": "481673fb", + "metadata": { + "editable": true + }, "source": [ "## Compare Bagging on Trees with Random Forests" ] @@ -2461,8 +2339,11 @@ { "cell_type": "code", "execution_count": 22, - "id": "bb5bea62", - "metadata": {}, + "id": "d5fe5939", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "bag_clf = BaggingClassifier(\n", @@ -2473,8 +2354,11 @@ { "cell_type": "code", "execution_count": 23, - "id": "b879f3ce", - "metadata": {}, + "id": "7ee6160d", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "bag_clf.fit(X_train, y_train)\n", @@ -2488,8 +2372,10 @@ }, { "cell_type": "markdown", - "id": "60160f97", - "metadata": {}, + "id": "3a6e484c", + "metadata": { + "editable": true + }, "source": [ "## Boosting, a Bird's Eye View\n", "\n", @@ -2506,8 +2392,10 @@ }, { "cell_type": "markdown", - "id": "b351b1bd", - "metadata": {}, + "id": "ed91ea28", + "metadata": { + "editable": true + }, "source": [ "## What is boosting? Additive Modelling/Iterative Fitting\n", "\n", @@ -2518,8 +2406,10 @@ }, { "cell_type": "markdown", - "id": "6e9174ef", - "metadata": {}, + "id": "9ab6f212", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n", @@ -2528,8 +2418,10 @@ }, { "cell_type": "markdown", - "id": "fc319721", - "metadata": {}, + "id": "288a57f1", + "metadata": { + "editable": true + }, "source": [ "where $\\beta_m$ are the expansion parameters to be determined in a\n", "minimization process and $b(x;\\gamma_m)$ are some simple functions of\n", @@ -2543,8 +2435,10 @@ }, { "cell_type": "markdown", - "id": "da4ba861", - "metadata": {}, + "id": "d0ea7a14", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\sigma(t) = \\frac{1}{1+\\exp{(-t)}},\n", @@ -2553,8 +2447,10 @@ }, { "cell_type": "markdown", - "id": "f444a5a4", - "metadata": {}, + "id": "faa1b446", + "metadata": { + "editable": true + }, "source": [ "where $t=\\gamma_0+\\gamma_1 x$ and the parameters $\\gamma_0$ and\n", "$\\gamma_1$ were determined by the Logistic Regression fitting\n", @@ -2565,8 +2461,10 @@ }, { "cell_type": "markdown", - "id": "8a4d8175", - "metadata": {}, + "id": "a20c6de5", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(\\boldsymbol{y},\\boldsymbol{f}) = \\frac{1}{n} \\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n", @@ -2575,8 +2473,10 @@ }, { "cell_type": "markdown", - "id": "de12bc14", - "metadata": {}, + "id": "17800601", + "metadata": { + "editable": true + }, "source": [ "In this case the function $f(x)$ was replaced by the design matrix\n", "$\\boldsymbol{X}$ and the unknown linear regression parameters $\\boldsymbol{\\beta}$,\n", @@ -2586,8 +2486,10 @@ }, { "cell_type": "markdown", - "id": "735bf417", - "metadata": {}, + "id": "7b59e224", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\boldsymbol{\\beta}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n", @@ -2596,16 +2498,20 @@ }, { "cell_type": "markdown", - "id": "22b8d82f", - "metadata": {}, + "id": "f5917a9a", + "metadata": { + "editable": true + }, "source": [ "In iterative fitting or additive modeling, we minimize the cost function with respect to the parameters $\\beta_m$ and $\\gamma_m$." ] }, { "cell_type": "markdown", - "id": "661db2e1", - "metadata": {}, + "id": "8d2101f2", + "metadata": { + "editable": true + }, "source": [ "## Iterative Fitting, Regression and Squared-error Cost Function\n", "\n", @@ -2630,8 +2536,10 @@ }, { "cell_type": "markdown", - "id": "2b14c81e", - "metadata": {}, + "id": "65f77895", + "metadata": { + "editable": true + }, "source": [ "## Squared-Error Example and Iterative Fitting\n", "\n", @@ -2644,8 +2552,10 @@ }, { "cell_type": "markdown", - "id": "64c44231", - "metadata": {}, + "id": "28b76851", + "metadata": { + "editable": true + }, "source": [ "$$\n", "(\\beta_m,\\gamma_m) = \\mathrm{argmin}_{\\beta,\\lambda}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta b(x;\\gamma))^2=\\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta(1+\\gamma x_i))^2.\n", @@ -2654,8 +2564,10 @@ }, { "cell_type": "markdown", - "id": "1040bdaf", - "metadata": {}, + "id": "985ccfb2", + "metadata": { + "editable": true + }, "source": [ "We start our iteration by simply setting $f_0(x)=0$. \n", "Taking the derivatives with respect to $\\beta$ and $\\gamma$ we obtain" @@ -2663,8 +2575,10 @@ }, { "cell_type": "markdown", - "id": "de59d269", - "metadata": {}, + "id": "05d75812", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial {\\cal C}}{\\partial \\beta} = -2\\sum_{i}(1+\\gamma x_i)(y_i-\\beta(1+\\gamma x_i))=0,\n", @@ -2673,16 +2587,20 @@ }, { "cell_type": "markdown", - "id": "5f87e844", - "metadata": {}, + "id": "9cfc5c74", + "metadata": { + "editable": true + }, "source": [ "and" ] }, { "cell_type": "markdown", - "id": "a2f9215c", - "metadata": {}, + "id": "3833eb7b", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial {\\cal C}}{\\partial \\gamma} =-2\\sum_{i}\\beta x_i(y_i-\\beta(1+\\gamma x_i))=0.\n", @@ -2691,16 +2609,20 @@ }, { "cell_type": "markdown", - "id": "67f71f90", - "metadata": {}, + "id": "3fca374c", + "metadata": { + "editable": true + }, "source": [ "We can then rewrite these equations as (defining $\\boldsymbol{w}=\\boldsymbol{e}+\\gamma \\boldsymbol{x})$ with $\\boldsymbol{e}$ being the unit vector)" ] }, { "cell_type": "markdown", - "id": "5410f260", - "metadata": {}, + "id": "aeb623d7", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\gamma \\boldsymbol{w}^T(\\boldsymbol{y}-\\beta\\gamma \\boldsymbol{w})=0,\n", @@ -2709,16 +2631,20 @@ }, { "cell_type": "markdown", - "id": "0485a1f5", - "metadata": {}, + "id": "f8e542a2", + "metadata": { + "editable": true + }, "source": [ "which gives us $\\beta = \\boldsymbol{w}^T\\boldsymbol{y}/(\\boldsymbol{w}^T\\boldsymbol{w})$. Similarly we have" ] }, { "cell_type": "markdown", - "id": "3a256711", - "metadata": {}, + "id": "d92e3136", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\beta\\gamma \\boldsymbol{x}^T(\\boldsymbol{y}-\\beta(1+\\gamma \\boldsymbol{x}))=0,\n", @@ -2727,8 +2653,10 @@ }, { "cell_type": "markdown", - "id": "fc8cd2ae", - "metadata": {}, + "id": "bca4d27a", + "metadata": { + "editable": true + }, "source": [ "which leads to $\\gamma =(\\boldsymbol{x}^T\\boldsymbol{y}-\\beta\\boldsymbol{x}^T\\boldsymbol{e})/(\\beta\\boldsymbol{x}^T\\boldsymbol{x})$. Inserting\n", "for $\\beta$ gives us an equation for $\\gamma$. This is a non-linear equation in the unknown $\\gamma$ and has to be solved numerically. \n", @@ -2739,8 +2667,10 @@ }, { "cell_type": "markdown", - "id": "0a9ecf4b", - "metadata": {}, + "id": "d2fa1316", + "metadata": { + "editable": true + }, "source": [ "## Iterative Fitting, Classification and AdaBoost\n", "\n", @@ -2753,8 +2683,10 @@ }, { "cell_type": "markdown", - "id": "ac605ae7", - "metadata": {}, + "id": "5401b686", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{\\overline{err}}=\\frac{1}{n} \\sum_{i=0}^{n-1} I(y_i\\ne G(x_i)).\n", @@ -2763,8 +2695,10 @@ }, { "cell_type": "markdown", - "id": "b4d530db", - "metadata": {}, + "id": "082464c4", + "metadata": { + "editable": true + }, "source": [ "The iterative procedure starts with defining a weak classifier whose\n", "error rate is barely better than random guessing. The iterative\n", @@ -2777,8 +2711,10 @@ }, { "cell_type": "markdown", - "id": "0f9fce0f", - "metadata": {}, + "id": "4f2a4a17", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n", @@ -2787,16 +2723,20 @@ }, { "cell_type": "markdown", - "id": "73471c17", - "metadata": {}, + "id": "4aad349a", + "metadata": { + "editable": true + }, "source": [ "will be a function of" ] }, { "cell_type": "markdown", - "id": "d8244842", - "metadata": {}, + "id": "d2050bb1", + "metadata": { + "editable": true + }, "source": [ "$$\n", "G_M(x) = \\mathrm{sign} \\sum_{i=1}^M \\alpha_m G_m(x).\n", @@ -2805,8 +2745,10 @@ }, { "cell_type": "markdown", - "id": "be09fe99", - "metadata": {}, + "id": "359b0eb3", + "metadata": { + "editable": true + }, "source": [ "## Adaptive Boosting, AdaBoost\n", "\n", @@ -2815,8 +2757,10 @@ }, { "cell_type": "markdown", - "id": "a547cf77", - "metadata": {}, + "id": "eab77ff9", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_m(x) = f_{m-1}(x)+\\beta_mG_m(x).\n", @@ -2825,8 +2769,10 @@ }, { "cell_type": "markdown", - "id": "67b1198a", - "metadata": {}, + "id": "d7c87ec5", + "metadata": { + "editable": true + }, "source": [ "The simplest possible cost function which leads (also simple from a computational point of view) to the AdaBoost algorithm is the\n", "exponential cost/loss function defined as" @@ -2834,8 +2780,10 @@ }, { "cell_type": "markdown", - "id": "f0a75e83", - "metadata": {}, + "id": "3cf53a5f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}\\exp{(-y_i(f_{m-1}(x_i)+\\beta G(x_i))}.\n", @@ -2844,8 +2792,10 @@ }, { "cell_type": "markdown", - "id": "9d2d96dc", - "metadata": {}, + "id": "67bbb3ac", + "metadata": { + "editable": true + }, "source": [ "We optimize $\\beta$ and $G$ for each value of $m=1:M$ as we did in the regression case.\n", "This is normally done in two steps. Let us however first rewrite the cost function as" @@ -2853,8 +2803,10 @@ }, { "cell_type": "markdown", - "id": "a6c2a558", - "metadata": {}, + "id": "d9a2448e", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}w_i^{m}\\exp{(-y_i\\beta G(x_i))},\n", @@ -2863,16 +2815,20 @@ }, { "cell_type": "markdown", - "id": "5a582df6", - "metadata": {}, + "id": "392f16f2", + "metadata": { + "editable": true + }, "source": [ "where we have defined $w_i^m= \\exp{(-y_if_{m-1}(x_i))}$." ] }, { "cell_type": "markdown", - "id": "654c5f13", - "metadata": {}, + "id": "a645e83c", + "metadata": { + "editable": true + }, "source": [ "## Building up AdaBoost\n", "\n", @@ -2881,8 +2837,10 @@ }, { "cell_type": "markdown", - "id": "efecb2bc", - "metadata": {}, + "id": "89e1d11e", + "metadata": { + "editable": true + }, "source": [ "$$\n", "G_m(x) = \\mathrm{sign} \\sum_{i=0}^{n-1} w_i^m I(y_i \\ne G_(x_i)),\n", @@ -2891,8 +2849,10 @@ }, { "cell_type": "markdown", - "id": "da78bb27", - "metadata": {}, + "id": "f884f217", + "metadata": { + "editable": true + }, "source": [ "which is the classifier that minimizes the weighted error rate in predicting $y$.\n", "\n", @@ -2901,8 +2861,10 @@ }, { "cell_type": "markdown", - "id": "dc1c118f", - "metadata": {}, + "id": "664acf19", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\exp{-(\\beta)}\\sum_{y_i=G(x_i)}w_i^m+\\exp{(\\beta)}\\sum_{y_i\\ne G(x_i)}w_i^m,\n", @@ -2911,16 +2873,20 @@ }, { "cell_type": "markdown", - "id": "1b77640d", - "metadata": {}, + "id": "d83ebe5e", + "metadata": { + "editable": true + }, "source": [ "which can be rewritten as" ] }, { "cell_type": "markdown", - "id": "d7944742", - "metadata": {}, + "id": "96134017", + "metadata": { + "editable": true + }, "source": [ "$$\n", "(\\exp{(\\beta)}-\\exp{-(\\beta)})\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i))+\\exp{(-\\beta)}\\sum_{i=0}^{n-1}w_i^m=0,\n", @@ -2929,16 +2895,20 @@ }, { "cell_type": "markdown", - "id": "94ffa0c4", - "metadata": {}, + "id": "97fc79c8", + "metadata": { + "editable": true + }, "source": [ "which leads to" ] }, { "cell_type": "markdown", - "id": "eae46622", - "metadata": {}, + "id": "0d1e4f3e", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\beta_m = \\frac{1}{2}\\log{\\frac{1-\\mathrm{\\overline{err}}}{\\mathrm{\\overline{err}}}},\n", @@ -2947,16 +2917,20 @@ }, { "cell_type": "markdown", - "id": "099f71b5", - "metadata": {}, + "id": "7d571a24", + "metadata": { + "editable": true + }, "source": [ "where we have redefined the error as" ] }, { "cell_type": "markdown", - "id": "11e5f200", - "metadata": {}, + "id": "8f31a3a7", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{\\overline{err}}_m=\\frac{1}{n}\\frac{\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i)}{\\sum_{i=0}^{n-1}w_i^m},\n", @@ -2965,16 +2939,20 @@ }, { "cell_type": "markdown", - "id": "77b52ed2", - "metadata": {}, + "id": "e0e3db77", + "metadata": { + "editable": true + }, "source": [ "which leads to an update of" ] }, { "cell_type": "markdown", - "id": "e8fe5df6", - "metadata": {}, + "id": "fc1ce185", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_m(x) = f_{m-1}(x) +\\beta_m G_m(x).\n", @@ -2983,16 +2961,20 @@ }, { "cell_type": "markdown", - "id": "4c1ea9b7", - "metadata": {}, + "id": "d7360d72", + "metadata": { + "editable": true + }, "source": [ "This leads to the new weights" ] }, { "cell_type": "markdown", - "id": "a61b875a", - "metadata": {}, + "id": "cf486320", + "metadata": { + "editable": true + }, "source": [ "$$\n", "w_i^{m+1} = w_i^m \\exp{(-y_i\\beta_m G_m(x_i))}\n", @@ -3001,8 +2983,10 @@ }, { "cell_type": "markdown", - "id": "a0df6e36", - "metadata": {}, + "id": "8ebdf8ce", + "metadata": { + "editable": true + }, "source": [ "## Adaptive boosting: AdaBoost, Basic Algorithm\n", "\n", @@ -3019,8 +3003,10 @@ }, { "cell_type": "markdown", - "id": "862806de", - "metadata": {}, + "id": "853a1218", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{err}=\\frac{1}{n}\\sum_{i=0}^{n-1}I(y_i\\ne G(x_i)),\n", @@ -3029,16 +3015,20 @@ }, { "cell_type": "markdown", - "id": "60c6b96e", - "metadata": {}, + "id": "d287b19d", + "metadata": { + "editable": true + }, "source": [ "where the function $I()$ is one if we misclassify and zero if we classify correctly." ] }, { "cell_type": "markdown", - "id": "d4cf16bb", - "metadata": {}, + "id": "637ef064", + "metadata": { + "editable": true + }, "source": [ "## Basic Steps of AdaBoost\n", "\n", @@ -3051,8 +3041,10 @@ }, { "cell_type": "markdown", - "id": "91e907b9", - "metadata": {}, + "id": "f1698dc3", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{\\overline{err}}_m=\\frac{\\sum_{i=0}^{n-1}w_i^m I(y_i\\ne G(x_i))}{\\sum_{i=0}^{n-1}w_i},\n", @@ -3061,8 +3053,10 @@ }, { "cell_type": "markdown", - "id": "cc913a38", - "metadata": {}, + "id": "c6d08c01", + "metadata": { + "editable": true + }, "source": [ "1. Then we start looping over all attempts at classifying, namely we start an iterative process for $m=1:M$, where $M$ is the final number of classifications. Our given classifier could for example be a plain decision tree.\n", "\n", @@ -3087,8 +3081,10 @@ }, { "cell_type": "markdown", - "id": "87e49535", - "metadata": {}, + "id": "c58db919", + "metadata": { + "editable": true + }, "source": [ "## AdaBoost Examples\n", "\n", @@ -3098,8 +3094,11 @@ { "cell_type": "code", "execution_count": 24, - "id": "a48ac6a2", - "metadata": {}, + "id": "104e1c7c", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.ensemble import AdaBoostClassifier\n", @@ -3126,25 +3125,7 @@ ] } ], - "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": "3.9.10" - } - }, + "metadata": {}, "nbformat": 4, 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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 17 (Midnight)","Project 3 on Machine Learning, deadline December 18 (midnight), 2023","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations","Week 44, Convolutional Neural Networks (CNN)","Week 45, Recurrent Neural Networks","Week 46: Decision Trees, Ensemble methods and Random Forests"],titleterms:{"1":[0,15,16,17,18,26,33,34],"12":40,"13":[],"14":36,"17":27,"18":28,"19":41,"2":[0,15,16,17,18,27,33,34,43],"2023":[26,28,31],"21":37,"23":37,"26":42,"3":[0,15,16,28,33,34],"31":34,"34":[15,33],"35":[16,34],"36":[17,35],"37":[18,36],"38":[19,37],"39":[20,21,38],"3d":43,"4":[0,34],"40":[21,39],"41":[21,40],"42":[22,41],"43":[23,42],"44":[23,42,43],"45":44,"46":45,"5":0,"7":35,"9":[26,44],"case":[8,10,30,34,35,37,38],"class":37,"do":[1,33,35,36,38,39,40,41,42,45],"final":[12,21,27,34,35,38,39,40,42,43,45],"function":[0,1,6,7,8,10,11,12,13,21,23,26,27,30,33,34,35,36,37,38,39,40,41,42,43,45],"import":[5,21,25,33,34,35,43,45],"long":44,"new":[4,35,36,44],"short":44,"try":44,A:[0,1,4,8,9,21,33,35,36,37,41,42,43,44,45],AND:[23,39,40,41,42],And:[21,33,34,35,37,38,39],But:[21,38,39],In:31,Is:[41,42],Ising:6,No:43,OR:[39,40,41,42],The:[0,1,2,3,5,6,7,8,9,11,12,17,23,24,28,33,34,35,36,37,38,39,40,41,42,43,44,45],To:[33,34],With:[4,35,44],about:[33,34],abov:[23,35,42,43],activ:[1,12,23,27,35,39,40,41,42,43,44],ad:[0,6,17,26,33,34,39,40],adaboost:[10,45],adagrad:[13,21,38,39],adam:[13,21,38,39],adapt:[10,21,38,39,45],add:43,addit:[43,44,45],adjust:[1,40,41,42],advanc:21,adversari:4,again:[3,9,37,43,44],ai:33,aim:[8,9,17,18,19,20,21,22,23,33,45],aka:[33,34],al:21,algebra:[25,33],algorithm:[9,10,11,12,21,27,34,38,39,40,41,45],algortithm:[13,37,38],all:[8,44],altern:28,an:[0,4,10,33,44,45],analys:[5,34],analysi:[0,5,6,11,24,26,27,30,33,34,35,36],analyt:[0,16,17,21,42],analyz:28,ani:[13,37,38],anoth:[9,35,36],appli:24,approach:[0,8,14,33,36,38,39,45],approxim:[12,40],architectur:[1,40,41,42],argument:[38,39],arithmet:43,arrai:[25,33],artifici:[39,40],assist:31,assumpt:[35,36],august:34,autocorrel:30,autograd:[2,13,21,38,39,42],automat:[13,21,38,39,42],avoid:[38,39],b:[17,26,27,28,38,39],back:[1,11,12,40,41,42],background:[24,26,27,36],backpropag:[43,44],backward:44,bag:[10,45],base:[13,21,36,38,39],basic:[0,5,7,9,10,11,25,28,34,35,36,37,44,45],batch:[1,38,39,41,42],bay:[5,35,36],befor:11,beta:[35,36],better:[8,39,40],bia:[6,26,36],bias:[40,41,42],binari:[1,40,41,45],bind:33,bird:[10,45],boldsymbol:[34,35,36],boost:[10,45],bootstrap:[6,10,36,45],boston:[0,34],breast:[1,41,42],brief:[33,36,37,38,43],bring:[12,40],build:[1,3,9,23,41,42,43,45],c:[26,27,28,33],calcul:34,can:[21,33,36,38,39,44],cancer:[1,7,9,11,37,41,42,44,45],cart:[9,45],cell:44,center:34,central:[13,24,30,36,37,38],chain:[12,40],challeng:37,chang:[10,45],channel:33,chi:[0,33],choic:[41,42],choos:[1,40,41,42],cifar01:[3,43],classic:11,classif:[1,9,10,23,27,37,40,41,42,44,45],classifi:[8,37,45],clip:[1,41,42],cluster:14,cnn:[3,43],code:[0,1,2,5,9,11,12,13,14,21,23,27,33,34,35,36,37,38,39,40,41,42,43,45],coeffici:43,coin:45,collect:[1,3,40,41,42,43],combin:44,come:37,commun:33,compact:37,compar:[2,10,42,45],comparison:35,compet:[21,38,39],compil:43,complet:34,complex:[0,6,26,28,34,45],complic:[6,38,39],compon:11,comput:[9,38,39,45],computation:36,computerlab:33,con:[9,45],concept:30,condit:[35,36,37,38],confid:36,conjug:[13,38],connect:43,construct:[40,41,42],content:43,continu:39,contn:33,convex:[8,13,37,38],convolut:[3,12,39,40,43],convolution2dlay:43,correctli:[35,36],correl:[11,34,37,43,44],correspond:[37,38],cost:[1,10,23,34,35,36,37,38,40,41,42,43,45],cours:[24,32,33],covari:[5,11,30,34],cover:33,critic:27,cross:[6,26,36,37],ct:43,cumul:44,curv:44,cython:33,d:[26,27,28],data:[0,1,3,6,7,9,11,15,16,23,24,26,28,30,33,34,35,37,40,41,42,43,44,45],dataset:[1,3,40,41,42,43],deadlin:[26,27,28,33],decai:[2,38,39,42],decemb:28,decis:[9,10,45],decomposit:[5,11,17,25,34],deep:[1,2,37,41,42],defin:[1,28,33,40,41,42],definit:40,degre:[0,34],deliveri:[26,27,28],delta:36,demonstr:43,dens:[0,33,43],deriv:[5,12,34,35,36,37,38,40,41,42],descent:[2,10,13,21,27,37,38,39,42],descript:26,design:34,detail:[3,33,42,43],develop:[1,40,41,42],diagon:11,differ:[8,27,38,39,43],different:21,differenti:[2,13,28,38,39,42],diffus:[2,42],dimension:[2,3,8,23,26,34,42,43],directli:[38,39],disadvantag:[9,45],discret:30,discuss:[37,44],distribut:[5,30,35,36],distrubut:[35,36],doe:[34,35,39,40],domain:[30,43],don:43,dot:[38,39],down:[1,41,42,45],dropout:[1,41,42],e:[26,27,28],each:37,economi:34,effect:44,effici:43,electron:[26,27,28],element:[0,30,33,38,39],elimin:25,energi:33,ensembl:[10,45],entropi:[9,37],environ:[0,15,33],equat:[0,2,12,28,33,34,35,37,38,40,42],error:[0,10,33,34,36,45],essenti:33,estim:[35,36],et:21,etc:[33,43],euler:[2,42],evalu:[1,27,40,41,42,43],exampl:[0,1,2,3,4,6,7,8,9,10,21,33,34,35,36,37,38,39,40,41,42,43,44,45],exercis:[0,6,15,16,17,18,19,20,21,22,23,33,34,36,42],expect:[18,30,35,36],expens:36,experi:30,explod:44,explor:[0,15,16,33],exponenti:[2,42],express:[17,18,33,34,37,38],extend:[37,38],extrapol:[4,44],extrem:[10,33,45],ey:[10,45],f:[26,27],f_1:44,factor:45,fall:31,famili:[1,33,34,41,42],famou:25,fantast:34,featur:[9,25,34,45],feed:[1,12,39,40,41,42],file:43,find:[36,38,39,43],fine:[1,41,42],first:[4,12,27,33,34,35,37,38,40,42,45],fit:[0,10,33,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dd_lay:43,add_outgrad:[],add_outputlay:43,add_poolinglay:43,add_subplot:[1,7,12,14,37,39,40,41,42],addendum:5,addit:[0,2,3,5,6,7,8,9,10,12,13,15,17,21,22,23,24,25,26,28,30,31,32,33,34,35,36,37,38,39,40,42],addition:[12,13,37,38,39,40,43],address:[1,9,11,13,33,38,39,41,42,43,45],adjac:[3,12,39,40,43],adjoint:[5,34,35],adjust:[0,5,12,13,37,38,39,44],admir:[0,33],adopt:43,advanc:[4,6,12,32,33,36,39,40,43],advantag:[1,3,5,6,10,13,25,35,36,37,38,39,40,41,42,43,45],adversari:33,afecionado:33,affect:[3,23,42,43],affin:[0,3,8,11,34,43],afford:[3,43],aficionado:33,aforement:14,african:[0,34],after:[0,1,2,4,5,6,9,11,12,13,15,20,21,23,24,25,26,27,28,30,33,34,35,36,38,39,40,41,42,43,44,45],afterward:[0,33],ag:[0,7,29,33,34,37],ag_0:[2,42],again:[0,1,4,5,6,7,8,10,11,12,13,15,16,21,22,23,26,27,30,33,34,35,36,38,39,40,41,42,45],against:[1,4,7,10,21,22,27,37,40,41,42,44,45],agegroup:[7,37],agegroupmean:[7,37],aggreg:[9,10,43,45],ago:44,agorithm:10,agre:[5,6,30,35,36],agreement:[13,21,38,39],ahead:[9,45],ai:[0,27,32],aid:[11,20,43],aim:[0,1,4,6,7,11,14,15,16,24,25,26,27,28,34,36,37,41,42],ainv:5,airplan:[3,43],aka:[5,35,36],al:[0,2,4,15,16,17,27,28,32,33,34,35,36,37,38,39,40,41,42,43,44,45],alarm:[5,7,35,36],albeit:43,algebra:[0,3,5,13,21,24,34,35,36,38,39,43],algorithm:[0,1,2,4,5,6,7,8,13,14,22,23,24,25,26,28,30,32,33,35,36,37,42,43,44],align:[0,2,5,6,7,8,13,26,30,33,34,35,36,37,38,42,43],all:[0,1,2,3,4,5,6,7,9,10,11,12,13,14,15,21,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,45],allevi:[1,13,37,38,41,42],alloc:[3,25,43],allow:[0,1,2,3,5,6,8,10,13,15,16,23,24,25,26,28,33,34,35,36,37,38,39,40,41,42,43,44,45],almost:[0,1,6,8,11,13,21,28,30,33,36,37,38,39,41,42],alon:[2,9,42],along:[2,3,4,5,6,9,10,11,23,24,25,28,33,34,35,36,37,42,43,44,45],alpha:[0,1,2,3,4,6,7,8,9,10,13,14,23,30,33,34,36,37,38,40,41,42,43,44,45],alpha_0:[3,43],alpha_1:[3,43],alpha_2:[3,43],alpha_:[10,45],alpha_i:[3,13,38,43],alpha_k:[13,38],alpha_m:[10,45],alpha_n:[3,43],alpha_opt:[13,38],alreadi:[2,3,4,5,6,10,12,24,25,30,33,34,35,36,39,40,42,43,45],also:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,21,23,24,25,26,27,28,29,30,33,34,35,36,37,38,39,40,41,42,43,44,45],alter:[1,40,41,42],altern:[0,1,4,5,6,7,8,9,11,13,25,26,33,34,35,36,37,38,39,40,41,42,43,44,45],although:[0,1,5,6,8,10,13,16,21,23,28,33,35,36,38,39,41,42,43,45],alwai:[0,3,5,6,12,13,16,21,30,33,34,35,36,37,38,39,40,43],am:[4,34,43],ame2016:[0,33],american:[0,34],among:[0,3,5,9,10,12,25,28,33,34,35,39,40,43,44,45],amongst:[5,35,36],amount:[0,1,3,4,6,8,10,14,23,24,36,41,42,43,45],an:[1,2,3,5,6,7,8,9,11,12,13,14,15,16,17,19,20,21,23,24,25,26,27,28,30,31,32,34,35,36,37,38,39,40,41,42,43],an_:30,anaconda:[0,1,15,24,26,28,33,41,42],analog:[13,38,39,44],analys:[6,28,35,36],analysi:[1,3,4,7,14,15,16,17,19,21,22,25,28,32,37,40,41,42,43,44],analyt:[2,3,5,6,7,12,13,15,22,24,26,27,28,33,34,35,36,37,38,39,40,43],analytical_gradi:21,analyz:[0,1,3,4,5,6,16,17,26,27,30,33,34,35,40,41,42,43,44],andrew:[1,40,41,42],angl:[0,3,9,34,43],anharmon:3,ani:[0,1,2,3,4,5,6,7,8,9,10,12,14,18,23,30,33,34,35,36,39,40,41,42,43,44,45],anim:[4,12,39,40],ann:[12,39,40],annot:[0,1,3,7,8,23,33,34,37,40,41,42,43,44],announc:33,anoth:[0,1,3,4,5,6,7,8,10,11,12,13,25,26,27,30,33,34,37,38,39,40,41,42,43,44,45],ans_vspac:2,ansatz:[0,33],answer:[0,1,3,5,6,25,26,27,28,31,33,35,36,40,41,42,43],antialias:[2,6,26,42],anticip:[4,44],anymor:[1,8,41,42],anyon:[4,8],anyth:[1,30,41,42,44],anytim:[31,33],apach:[1,41,42],apart:[11,13,37,38],api:[1,24,33,41,42,43],appar:[2,42],appear:[0,1,3,13,16,23,25,30,33,38,39,40,41,42,43],append:[1,3,4,8,9,13,21,23,33,38,41,42,43,44,45],appendix:26,appl:[3,4,43,44],appli:[0,1,2,3,4,6,7,8,9,10,11,12,13,15,16,26,27,30,32,33,34,35,36,37,38,39,40,41,42,43,45],applic:[0,1,3,4,5,6,7,9,12,13,21,25,26,30,32,33,34,36,37,38,39,40,41,42,43,44,45],apply_gradi:4,approach:[1,2,4,5,6,9,10,11,12,13,17,21,23,24,28,30,32,34,37,40,41,42,43,44],appropri:[2,6,9,12,13,24,30,36,38,39,40,42,43],approv:33,approx:[0,2,3,6,10,11,13,28,30,33,36,37,38,39,42,43,45],approxim:[0,1,2,3,4,5,6,7,10,11,13,18,19,26,30,33,34,35,36,37,38,39,41,42,43,44,45],apt:[0,15,24,26,28,33],aq:30,ar:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,19,21,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],aragorn:33,arang:[1,3,4,6,7,9,10,12,13,23,26,33,37,38,39,40,41,42,43,44,45],arbitrari:[1,4,6,8,12,13,30,36,37,38,39,40,41,42,44],arbitrarili:[0,1,11,33,40,41,42],arc:[6,26],architectur:[3,4,12,23,43,44],archiv:27,area:[0,3,6,9,26,32,33,43,45],arg:[0,2,3,4,13,33,39,43,44],argmax:[1,11,40,41,42,43],argmin:[4,10,14,45],argnum:[2,13,39],argnum_0:[],argnum_1:[],args_with_tang:[3,4,43,44],argsort:11,argu:[1,13,38,39,41,42],argument:[0,2,3,5,6,11,12,13,21,23,26,33,34,35,36,40,42,43],argval:2,aris:[0,6,12,13,30,33,36,37,38,40],arithmet:[0,13,25,33,38,39],arm:[6,35],armadillo:25,around:[0,1,4,5,6,11,30,33,35,36,40,41,42],arr:2,arrai:[0,1,2,3,4,5,6,7,8,9,11,12,13,14,21,23,24,26,30,34,35,36,37,38,39,40,41,42,43,44],arrang:[3,33,43],arraybo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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 17 (Midnight)","Project 3 on Machine Learning, deadline December 18 (midnight), 2023","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations","Week 44, Convolutional Neural Networks (CNN)","Week 45, Recurrent Neural Networks","Week 46: Decision Trees, Ensemble methods and Random 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\ No newline at end of file diff --git a/doc/LectureNotes/_build/html/week46.html b/doc/LectureNotes/_build/html/week46.html index 81d696de3..b27eef4bc 100644 --- a/doc/LectureNotes/_build/html/week46.html +++ b/doc/LectureNotes/_build/html/week46.html @@ -1119,7 +1119,8 @@ doconce format html week46.do.txt --no_mako -->

  • Readings and Videos:

    @@ -1288,9 +1289,9 @@ predicting the target features of query instances is as follows:

    2nd degree coefficients:
-zero power:  3.7228270501360416
-first power:  0.125304962667421
-second power:  -0.0008187895061620525
+zero power:  -7.926488923435684
+first power:  0.10587157250737213
+second power:  -0.00018629164426077816
    _images/week46_9_1.png diff --git a/doc/LectureNotes/_build/jupyter_execute/week46.ipynb b/doc/LectureNotes/_build/jupyter_execute/week46.ipynb index 28b7d9d3e..4d14c11e1 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week46.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week46.ipynb @@ -2,8 +2,10 @@ "cells": [ { "cell_type": "markdown", - "id": "f4d3b2c9", - "metadata": {}, + "id": "f9935da5", + "metadata": { + "editable": true + }, "source": [ "\n", @@ -12,8 +14,10 @@ }, { "cell_type": "markdown", - "id": "57cda95f", - "metadata": {}, + "id": "0b990b1c", + "metadata": { + "editable": true + }, "source": [ "# Week 46: 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", @@ -23,8 +27,10 @@ }, { "cell_type": "markdown", - "id": "0ab525ed", - "metadata": {}, + "id": "71e9f143", + "metadata": { + "editable": true + }, "source": [ "## Plan for week 46\n", "\n", @@ -44,7 +50,9 @@ "\n", " * These lecture notes\n", "\n", - " * [Video of lecture to be added](https://youtu.be/)\n", + " * [Video of lecture](https://youtu.be/PMswUwhYa7k)\n", + "\n", + " * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov16.pdf) \n", "\n", " * [Video on Decision trees](https://www.youtube.com/watch?v=RmajweUFKvM&ab_channel=Simplilearn)\n", "\n", @@ -53,8 +61,10 @@ }, { "cell_type": "markdown", - "id": "3c8e0d42", - "metadata": {}, + "id": "00b82c38", + "metadata": { + "editable": true + }, "source": [ "## Decision trees, overarching aims\n", "\n", @@ -82,8 +92,10 @@ }, { "cell_type": "markdown", - "id": "893c9b6f", - "metadata": {}, + "id": "c733808f", + "metadata": { + "editable": true + }, "source": [ "## Basics of a tree\n", "\n", @@ -100,8 +112,10 @@ }, { "cell_type": "markdown", - "id": "89b0fc63", - "metadata": {}, + "id": "2b60a24e", + "metadata": { + "editable": true + }, "source": [ "## A typical Decision Tree with its pertinent Jargon, Classification Problem\n", "\n", @@ -116,8 +130,10 @@ }, { "cell_type": "markdown", - "id": "d4354730", - "metadata": {}, + "id": "1a8dc5f7", + "metadata": { + "editable": true + }, "source": [ "## General Features\n", "\n", @@ -137,8 +153,10 @@ }, { "cell_type": "markdown", - "id": "9db7330a", - "metadata": {}, + "id": "188465ed", + "metadata": { + "editable": true + }, "source": [ "## How do we set it up?\n", "\n", @@ -158,8 +176,10 @@ }, { "cell_type": "markdown", - "id": "331ebf1d", - "metadata": {}, + "id": "7f927d1c", + "metadata": { + "editable": true + }, "source": [ "## Decision trees and Regression" ] @@ -167,22 +187,25 @@ { "cell_type": "code", "execution_count": 1, - "id": "122986df", - "metadata": {}, + "id": "92377283", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2nd degree coefficients:\n", - "zero power: 3.7228270501360416\n", - "first power: 0.125304962667421\n", - "second power: -0.0008187895061620525\n" + "zero power: -7.926488923435684\n", + "first power: 0.10587157250737213\n", + "second power: -0.00018629164426077816\n" ] }, { "data": { - "image/png": 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CGSNJEiEhITx79qy4myIIOmVjY0OFChUKVGdPBD6CIAhljCbocXJywszMTBRjFUo9SZKIj48nNDQUgIoVK+b7XCLwEQRBKEPS0tKUoMfe3r64myMIOmNqagpAaGgoTk5O+R72EsnNgiAIZYgmp8fMzKyYWyIIuqf5vS5I7poIfARBEMogMbwllEW6+L0WgY8gCIIgCOVGqQp8jh07Rq9evahUqRIqlYpt27Zp3T9s2DBUKpXW1rJly+JprCAIglCquLm5sWjRouJuhk7cu3cPlUrF5cuXATh69CgqlUrM9KOUBT5xcXE0aNCAn376KdtjXn31VYKDg5Vt9+7dRdhCQRAEIb/Sf3k1MDCgSpUqjBo1iqioqOJuWqHZu3cvKpWKkJAQrf0VKlTA1dVVa9+jR49QqVTs37+/SNqW/udhaGiIs7MzXbp0YfXq1ajV6jyda+3atdjY2BROQ/OoVAU+3bt3Z+bMmfTv3z/bY4yNjalQoYKy2dnZFWELBeGFZ4nPuP/sPuHx4cq+VHUqSalJWR4vSRKPYh5x/9l9ElMTi6qZglCiaL683rt3j1WrVrFjxw5Gjx5d3M0qNG3btsXAwICjR48q+wICAkhMTCQmJobbt28r+48cOYKhoSFt2rQpsval/3ns2bOHDh068Mknn9CzZ09SU1OLrB26VKoCn9w4evQoTk5O1KpViw8++ECZ85+dpKQkYmJitDZBKKhTD0/hMM8Bt8VuOM134p+Af5AkiZarWlLzx5okpCRkesxHuz/C9QdX3Ba7UX1JdeKS44qh5YJQvDRfXitXrkzXrl0ZNGiQVg9HWloaI0aMwN3dHVNTU2rXrs3ixYu1zjFs2DD69u3L999/T8WKFbG3t2fMmDFaM4FCQ0Pp1asXpqamuLu788cff2Rqy4MHD+jTpw8WFhZYWVkxcOBAnj59qtw/ffp0GjZsyOrVq6lSpQoWFhaMGjWKtLQ05s2bR4UKFXBycuK7777L9vlaWFjQrFkzrcDn6NGjtG3blrZt22ba37x5c8zNzdm7dy9t27bFxsYGe3t7evbsyZ07d3L9OickJNCjRw9atmxJZGRktsdpfh4uLi40btyYKVOm8O+//7Jnzx7Wrl2rHLdw4ULq1auHubk5rq6ujB49mtjYWKXd7733HtHR0UoP0vTp0wH4/fffadq0KZaWllSoUIHBgwe/9HO7oMpU4NO9e3f++OMPDh8+zIIFCzh//jwdO3YkKSnrb9gAs2fPxtraWtkydi0KQn7svb2XNCkNAAmJ3bd2ExIbwsXgizyMeci10GuZHrPj5g7l/0+eP+FyyOWiaq5QxkmSRFxyXLFskiTlu913795l7969GBoaKvvUajWVK1dm8+bN+Pv7M23aNKZMmcLmzZu1HnvkyBHu3LnDkSNHWLduHWvXrtX6oB42bBj37t3j8OHD/P333yxdulTrA1eSJPr27UtkZCQ+Pj4cOHCAO3fuMGjQIK3r3Llzhz179rB37142bNjA6tWr6dGjB48ePcLHx4e5c+cydepUzpw5k+3z7NChA0eOHNFqe/v27fH29s60v0OHDoCc+jF+/HjOnz/PoUOH0NPTo1+/frkagoqOjqZr164kJydz6NChPI+MdOzYkQYNGrB161Zln56eHkuWLOH69eusW7eOw4cPM3HiRABat27NokWLsLKyUtJQPv/8cwCSk5OZMWMGV65cYdu2bQQFBTFs2LA8tSevylQBw/S/kF5eXjRt2pSqVauya9eubIfHJk+ezPjx45XbMTExIvgRCswvzA+AFi4tOPv4LH5hfviH+b+4P9SP5i7NldsxSTE8jHkIQHOX5px7fA6/MD/aVCm6Lm2h7IpPicditkWxXDt2cizmRua5Pn7nzp1YWFiQlpZGYqI85Ltw4ULlfkNDQ7755hvltru7O6dOnWLz5s0MHDhQ2W9ra8tPP/2Evr4+derUoUePHhw6dIgPPviAmzdvsmfPHs6cOUOLFi0A+PXXX6lbt67y+IMHD3L16lWCgoKUz4TffvsNT09Pzp8/T7NmzQA5EFu9ejWWlpZ4eHjQoUMHAgMD2b17N3p6etSuXZu5c+dy9OjRbCfbtG/fnlmzZhEcHEzFihXx8fFhwoQJqNVqpTfr4cOHBAUFKYHP66+/rnWOX3/9FScnJ/z9/fHy8sr29X369CmDBg2ievXqbNiwASMjo5f8RLJWp04drl69qtz+9NNPlf+7u7szY8YMRo0axdKlSzEyMsLa2hqVSkWFChW0zjN8+HDl/9WqVWPJkiU0b96c2NhYLCwK53e2TPX4ZFSxYkWqVq3KrVu3sj3G2NgYKysrrU0QCkoT5Az0lP8Q+4X5KcGQ5nZ6AWEBAFS0qEhb17Za5xCE8qRDhw5cvnyZs2fPMnbsWLp168bYsWO1jvnll19o2rQpjo6OWFhYsHLlSh48eKB1jKenp1Zl34oVKyo9OgEBARgYGNC0aVPl/jp16mgl3wYEBODq6qr1RdjDwwMbGxsCAgKUfW5ublhaWiq3nZ2d8fDwQE9PT2tfTsM3bdq0wcjIiKNHj+Lv709CQgKNGzemSZMmxMTEcOvWLY4cOYKxsTGtW7cG5J6mwYMHU61aNaysrHB3dwfI9Dpk1LlzZ6pVq8bmzZvzHfSA3COWvqbOkSNH6NKlCy4uLlhaWvLuu+8SERFBXFzOQ/a+vr706dOHqlWrYmlpSfv27XP1PAqiTPX4ZBQREcHDhw8LtKaHIORVUmoStyLkYLtvnb5MPDCRmKQY9t95kaeQMajRBEKeTp54Onlq7ROEgjIzNCN2cmyxXTsvzM3NqVGjBgBLliyhQ4cOfPPNN8yYMQOAzZs3M27cOBYsWECrVq2wtLRk/vz5nD17Vus86YfHQC58pxkG0gy/5VQML+MHe3b7s7pOTtfOipmZGc2bN+fIkSNERkbStm1bJWhr3bo1R44c4fTp07Rq1QoTExMAevXqhaurKytXrqRSpUqo1Wq8vLxITk7O9joAPXr0YMuWLfj7+1OvXr0cj81JQECAEmzdv3+f1157jZEjRzJjxgzs7Ow4ceIEI0aMyLHCclxcHF27dqVr1678/vvvODo68uDBA7p16/bS51EQpSrwiY2N1cpwDwoK4vLly9jZ2WFnZ8f06dN5/fXXqVixIvfu3WPKlCk4ODjQr1+/Ymy1UN7cjLhJmpSGlbEV7jbu1LCrQWBEoFbgkzGo8QuVb3s4eODh6KG1TxAKSqVS5Wm4qST5+uuv6d69O6NGjaJSpUocP36c1q1ba830yktSL0DdunVJTU3lwoULNG8uDzkHBgZq1bjx8PDgwYMHPHz4UOn18ff3Jzo6WmtITFc6dOjAxo0biYqKUno9ALy9vTl69CinT5/mvffeA+Qv9QEBASxfvpxXXnkFgBMnTuTqOnPmzMHCwoJOnTpx9OhRPDw88tzWw4cPc+3aNcaNGwfAhQsXSE1NZcGCBUpPV8acKyMjI9LS0rT23bhxg/DwcObMmaO8xhcuXMhze/KqVA11XbhwgUaNGtGoUSMAxo8fT6NGjZg2bRr6+vpcu3aNPn36UKtWLYYOHUqtWrU4ffq0VjekIBQ2pffG0ROVSqX04KSoX3zzeRD9gOdJzzM/xslTCXyCY4OJSii79UsEITfat2+Pp6cns2bNAqBGjRpcuHCBffv2cfPmTb766ivOnz+fp3PWrl2bV199lQ8++ICzZ89y8eJF3n//fWURTJCHhOrXr8+QIUO4dOkS586d491338Xb21triExXOnTowK1bt9i7dy/e3t7Kfm9vb3bu3Mm9e/eU/B5bW1vs7e1ZsWIFt2/f5vDhw1q5qi/z/fffM2TIEDp27MiNGzdyPDYpKYmQkBAeP37MpUuXmDVrFn369KFnz568++67AFSvXp3U1FR+/PFH7t69y2+//cYvv/yidR43NzdiY2M5dOgQ4eHhxMfHU6VKFYyMjJTHbd++XenZK0ylKvBp3749kiRl2tauXYupqSn79u0jNDSU5ORk7t+/z9q1a0WislDkND01no6eWv8C6Kn0sDeVV8xOP9yl+b+noydWxla4WrlmOkYQyqvx48ezcuVKHj58yMiRI+nfvz+DBg2iRYsWRERE5KvOz5o1a3B1dcXb25v+/fvz4Ycf4uTkpNyvWR3A1taWdu3aKbkxmzZt0uVTU7Rq1QpjY2MAmjRpouxv1qwZaWlpmJqaKonYenp6bNy4kYsXL+Ll5cW4ceOYP39+nq73ww8/MHDgQDp27MjNmzezPW7v3r1UrFgRNzc3Xn31VY4cOcKSJUv4999/leG4hg0bsnDhQubOnYuXlxd//PEHs2fP1jpP69atGTlyJIMGDcLR0ZF58+bh6OjI2rVr+euvv/Dw8GDOnDl8//33eXoe+aGSCjLXsAyKiYnB2tqa6Ohokegs5Mvrm19na8BWfuj2A5+2/JRN1zfx5pY3AahlXwtXK1cOBR3i196/MrzRcGKSYrCeYw1A5MRIbE1t6f5Hd/be3svynsv5sMmHxfl0hFImMTGRoKAg3N3dlXwQQSgrcvr9zu3nd6nq8RGE0kDJ1/lvyEoz1KXZp+kB0vTmaP6taFERW1Nb+TgHkecjCIJQGEpVcrMglHRJqUncjpQT8DUBTi37Wuir9EmT0vB09KSKdRXgRV6PMjSWLkASM7sEQRAKh+jxEQQdCowIJE1Kw9rYmkqWlQAw0jeiln0tQA6GMs7aSp/fo5GxV0gQBEHQDdHjIwg6pAQxTp5atT5mdJjB1htb6V27N8lpcn2KhzEPiUmK0ZoFppFxZpdmCEwQBEEoGNHjIwg6lHFGl8brHq/zR/8/MDcyx9bUlooWclFN/zB/JfDRBDsAlsaWyswuMdwlCIKgOyLwEQQdyiqIyYomh+f0w9M8inmktS/jMSLBWRAEQXdE4CMIOpTVsFVWNPf/HfA3AJUsK2FjYpPlMaLHRxAEQXdE4CMIOqI1o8sp58BH0yN06uEp+fgsAiWR4CwIgqB7IvARBB0JjAhELamxMbFRcniykzHQyWpoTJn9JXp8BEEQdEYEPoKgI+kLF+a06jNkkc+TRY+PJvAJiQ0hMiFSR60UhPLn3r17qFQqLl++XNxNKXHc3NxYtGhRcTejSInARxB0JLf5PQA2JjZKnR/IemjM0tjyRbFDkeAslAOzZ8+mWbNmWFpa4uTkRN++fQkMDCySa7dv3x6VSoVKpcLY2BgXFxd69erF1q1bi+T6hSn9c0u/paamcv78eT788MWyOJo1ysoyEfgIgo7kJfAB7eGt7GaBiTwfoTzx8fFhzJgxnDlzhgMHDpCamkrXrl2Ji4srkut/8MEHBAcHc/v2bbZs2YKHhwdvvvmmVmBQWJKTkwv1/Jrnln4zMDDA0dERMzOzQr12SSMCH0HQkfTFC3NDE9RkNaNLQxMQrb2ylu+OfUdEfAQAPvd82Hh9YwFbrDtnH51lje+a4m6GUMrt3buXYcOG4enpSYMGDVizZg0PHjzg4sWLyjFubm7MmjWL4cOHY2lpSZUqVVixYoXWec6dO0ejRo0wMTGhadOm+Pr65ur6ZmZmVKhQAVdXV1q2bMncuXNZvnw5K1eu5ODBg8pxjx8/ZtCgQdja2mJvb0+fPn24d++ecn9qaioff/wxNjY22NvbM2nSJIYOHUrfvn2VY9q3b89HH33E+PHjcXBwoEuXLgD4+/vz2muvYWFhgbOzM++88w7h4eHK4yRJYt68eVSrVg1TU1MaNGjA33//nevnln7TvJ6aoS43NzcA+vXrh0qlUm6XNSLwEQQdSExNVGZ0vayGj0bDCg21/s3pmDOPzjD1yFQWnl6IWlLTb1M/3tryFnej7hak2TozeOtghm8fzsUnF19+sFD0JAni4opnk6R8Nzs6OhoAOzs7rf0LFixQAprRo0czatQobty4AUBcXBw9e/akdu3aXLx4kenTp/P555/nuw1Dhw7F1tZWGfKKj4+nQ4cOWFhYcOzYMU6cOIGFhQWvvvqq0mszd+5c/vjjD9asWcPJkyeJiYnJcvho3bp1GBgYcPLkSZYvX05wcDDe3t40bNiQCxcusHfvXp4+fcrAgQOVx0ydOpU1a9awbNky/Pz8GDduHG+//TY+Pj75fo4a58+fB2DNmjUEBwcrt8scSdASHR0tAVJ0dHRxN0UoRS4HX5aYjmQzx0ZSq9W5ekxCSoI0/+R8KSAsINtjElMSpVnHZkmv/v6qxHSk1/54TQqKCpKYjsR0pK3+W3X1FPLtWcIzpT2rLq4q7uaUewkJCZK/v7+UkJDwYmdsrCTJIUjRb7Gx+XoearVa6tWrl9S2bVut/VWrVpXefvttreOcnJykZcuWSZIkScuXL5fs7OykuLg45Zhly5ZJgOTr65vt9by9vaVPPvkky/tatGghde/eXZIkSfr111+l2rVra73Pk5KSJFNTU2nfvn2SJEmSs7OzNH/+fOX+1NRUqUqVKlKfPn20rtewYUOt63z11VdS165dtfY9fPhQAqTAwEApNjZWMjExkU6dOqV1zIgRI6S33norx+dmaGgomZubK9v48eMlSZJfzx9++EE5FpD++eefbM9V3LL8/f5Pbj+/xVpdgqAD6fN7XjajS8PEwITPW+f8TdTYwJjJr0ym7f227L29F79QP61EZ78wP/rV7Zf/hutA+vwjMfVe0JWPPvqIq1evcuLEiUz31a9fX/m/SqWiQoUKhIaGAhAQEECDBg208lZatWpVoLZIkqS8ry9evMjt27extLTUOiYxMZE7d+4QHR3N06dPad68uXKfvr4+TZo0Qa1Waz2madOmWrcvXrzIkSNHsLCwyNQGzbkTExOVYTGN5ORkGjVqlONzGDJkCF9++aVy28bGJsfjyzIR+AiCDmS1wrouaYbP7kff5+zjs8r+rAKNyMhITp48ia+vL4GBgdy7d4+wsDBiYmJQq9UYGhpia2uLs7MzNWvWxMPDg5YtW9KwYUOMjIzy3Lb0bRCBTwllZgaxscV37TwaO3Ys27dv59ixY1SuXDnT/YaGhlq3VSqVElRIBRhay0paWhq3bt2iWbNmAKjVapo0acIff/yR6VhHR0etNqWXVbvMzc21bqvVanr16sXcuXMzHVuxYkWuX78OwK5du3BxcdG639jYOMfnYW1tTY0aNXI8prwQgY8g6IDS45PLxOa8sjezx9ncmadxT/nb/0Uio1+oH5IkcfnyZXbu3MmePXs4e/Zspm+WGT158gQ/Pz8OHz6s7LO0tKRLly68/vrr9O3bN9czPbR6fMS0+5JJpYIMH7IlkSRJjB07ln/++YejR4/i7u6e53N4eHjw22+/kZCQgKmpKQBnzpzJd5vWrVtHVFQUr7/+OgCNGzdm06ZNODk5YWVlleVjnJ2dOXfuHK+88gogB0++vr40bNgwx2s1btyYLVu24ObmhoFB5o9nDw8PjI2NefDgAd7e3vl+TjkxNDQkLS2tUM5dUojARxB0IH3xwsLi6eTJ06CnBIQHyDuSwH+PP41WNOLK5Stax9apU4dmzZrh5eVFtWrVcHZ2xsrKCn19fZKTk4mMjOTx48fcvHmTK1eucPr0aSIjI9m6dStbt27FwsKCd955h48//pg6derk2K70vTyPnz8mOjEaaxNrnT9/oewbM2YMf/75J//++y+WlpaEhIQAcm+FJoh5mcGDB/Pll18yYsQIpk6dyr179/j+++9z9dj4+HhCQkJITU3l8ePHbN26lR9++IFRo0bRoUMHQB4ymj9/Pn369OHbb7+lcuXKPHjwgK1btzJhwgQqV67M2LFjmT17NjVq1KBOnTr8+OOPREVFvXQYfMyYMaxcuZK33nqLCRMm4ODgwO3bt9m4cSMrV67E0tKSzz//nHHjxqFWq2nbti0xMTGcOnUKCwsLhg4dmqvnmRM3NzcOHTpEmzZtMDY2xtbWtsDnLGlE4CMIBZSYmsidqDtA4Q11ac59OOgwPAdOAL6QlpzGFa5gYmLCq6++Svfu3enevTuurq55OrdarebixYts376dP/74g6CgIJYtW8ayZcsYMGAA06dPx8Mj66AuYy+Pf5g/rVwLllMhlE/Lli0D5Kne6a1Zs4Zhw4bl6hwWFhbs2LGDkSNH0qhRIzw8PJg7d67SY5OTlStXsnLlSoyMjLC3t6dJkyZs2rSJfv1e5NGZmZlx7NgxJk2aRP/+/Xn+/DkuLi506tRJ6QGaNGkSISEhvPvuu+jr6/Phhx/SrVs39PX1c7x+pUqVOHnyJJMmTaJbt24kJSVRtWpVXn31VfT05EnYM2bMwMnJidmzZ3P37l1sbGxo3LgxU6ZMydXr8zILFixg/PjxrFy5EhcXF61p+mWFStL1gGgpFxMTg7W1NdHR0dl2YwpCeldCrtBweUNsTWyJmBiR6+TmvFpwcAGfT/8czgKp/+20h/c+eI/vJ3yfacpvfkmSxJEjR1i0aBE7duwAQE9Pj//973/MmDEDe3t75dhnic+wnSt/I2zu0pxzj8+xstdK3m/8vk7aIuRdYmIiQUFBuLu7Y2JiUtzNEZC/WNStW5eBAwcyY8aM4m5OqZbT73duP79FHR9BKKD0+T2FEfSkpqaycOFCvu7/NZwEUsHU3ZTOX3eGj8D9VXedBT0gJ2V27NiR7du3c+XKFfr27YtarWbZsmXUqVNHq4R/QJg87OZi6ULryq0BkecjCPfv32flypXcvHmTa9euMWrUKIKCghg8eHBxN01ABD6CUGBKfo+D7vN7zp8/T7Nmzfjss8+Iex4HzsBg6D2vtzylVQX+4YW3nEX9+vX5559/OHLkCF5eXoSHh/P666/z7rvv8uzZM62gT5PYXZjtEYTSQE9Pj7Vr19KsWTPatGnDtWvXOHjwIHXr1i3upgmIwEcoB5LTkvlwx4f85fdXlvfPOj6L7459p7Xv/OPz9PyzJ11+68KIf0eQlJqU6XGXgi/Ra0Mv1lyWl2rQ5Yyu+Ph4PvnkE1q0aMHly5extbVl1apVOH/mDLXAy8lLyScqih6W9u3bc/HiRaZMmYKenh6//fYblWpU4qu1XwFy0FeU7RGEkszV1ZWTJ08SHR2tJB+3a9euuJsl/EcEPkKZt//OflZeWsmEAxMy3RcaF8qXh79k6pGpPI19quyfd2oeu27t4uDdg6y+vJr9d/Zneuz8U/PZeXMnwbHBALSs3FIn7b18+TJNmzZlyZIlSJLEO++8w40bNxgxYgStq7RWrqWZQRYYEUiqOjWnU+qEkZER3333HSdOnMDWxZaEiARCfg6Bi9rtefz8Mc8SnxV6ewRBEPJDBD5CmXc9VC76dT/6Ps+Tnmvdl7EKcsbHOJs7a93O6ryT207GZ5gPzV2aZzomL9RqNYsWLaJFixYEBARQsWJF9u3bx/r163FycgJgVe9VHB16lE7unahqUxUzQzOS05KVdcKKQqtWrWg5oyV4AmpgB5xccRJzA3NcLOWiamI1eUEQSioR+AhlXvqAJuMHslbV4f+CoKTUJG5F3AJggMeATMcBpKSlEBgeCMCHTT6kXdWCdWNHR0fTp08fxo0bR3JyMr179+bq1at07dpV6zg7Uzu83bxRqVToqfSUXpaiDjQCnwfCG/De+PcA+PHHH3nttdeobVm7WNojCIKQWyLwEcq89L06GT+Qs1pn6mbETdKkNKyNrelSvYvWfRp3ou6Qok7B3NCcKtZVCtS+gIAAmjdvzs6dOzE2Nmbp0qVs27YNBweHlz62OPJq4lPiCYoKAhXM+XYOW7duxdzcnAMHDuC/0B8SRJ6PIAgllwh8hDItTZ32otIxmQOYrNaZ0pqp9F9gcSP8BmnqF2Xc01dq1lPl/23077//0qJFC27evKkkRI4aNSrX0+I1PT5FuUbWjfAbSEg4mDngZO5Ev3798PHxwc7OjpAbIbAOfO/6Fll7BEEQ8kIEPkKZdu/ZPRJTE5Xb6QMESZK0c3z+W/dK0wvk4eCBm40bJgYmJKYmEvQsKNN58rtEhSRJzJ07l759+/L8+XO8vb25cOECTZo0ydN5lB6fIgx8NK9Z+irVTZo04ejRo9g62EIInJxxkuDg4CJrkyAIQm6JwEco0zQBgYGevDpL+kAnNC6UiIQIVMj5MlGJUTyNe6rV46Ovp09dh7qZHqsck48lKlJTUxk1ahRffPEFIK9EfeDAASWBOS80U+gDw4tmZhdkH/TVq1eP/Yf2gyWkhqTSoWMHIiIiiqRNgvAy06dPf+kioYJs7dq12NjYFHczCo0IfIQyTROsdK7WGYCHMQ+JSYoBXuT3VLOtRjXbasrxGXs0NMFFVknSea3dExsbS58+fVi+fDkqlYrFixezZMkSDA0N8/X8qlhXwdzQnBR1SpHN7Mop6GtavykVPq4AVhB4I5AePXoQGxtbJO0SSr9jx47Rq1cvKlWqhEqlYtu2bZmOkSSJ6dOnU6lSJUxNTWnfvj1+fto9ntk9Nq+OHj2KSqWSJxPo6WFtbU2jRo2YOHFiqe/RTP/c0m9Tp05l0KBB3Lx5Uzm2rAWNIvARyjRNFeFXqrxCBYsKwItlFrLK5bkUfEkJIDRBTcbhpPQzuvLS4xMWFkb79u3ZvXs3JiYmbNmyhY8//rhAz09PpUddx8w9UoXpZUFfgzoN4B0wtzbn7NmzvPHGGyQnJxdJ24TSLS4ujgYNGvDTTz9le8y8efNYuHAhP/30E+fPn6dChQp06dKF58+fZ/uYggoMDOTJkyecP3+eSZMmcfDgQby8vLh27VqhXRPkIC81tXB7cgMDAwkODla2L774AlNT03z1QJcWIvARyrT0vTcZA5j0S01o7vvnxj/KjK6KFhXl+zUJxP8dfzvytjKjy9U6d6ugP3r0iHbt2nHx4kUcHBw4cuSI1orPBVGUeT7KjC6yD/o8HD3AEXpO74mZmRn79u1j2LBhqNXqQm+fULp1796dmTNn0r9//yzvlySJRYsW8eWXX9K/f3+8vLxYt24d8fHx/PnnnwC4ubkB0K9fP1QqlXJb47fffsPNzQ1ra2vefPPNXAVMTk5OVKhQgVq1avHmm29y8uRJHB0dGTVqlNZxa9asoW7dupiYmFCnTh2WLl2qdf+pU6do2LAhJiYmNG3alG3btqFSqbh8+TLwohdm3759NG3aFGNjY44fP44kScybN49q1aphampKgwYN+Pvvv7XO7e/vz2uvvYaFhQXOzs688847hIeH5/q5aTYLCwutoa61a9fyzTffcOXKFaVXaO3atS89b0kmAh+hVFJLanr+2RPbubbKVnFBRbYGvFhAM/2MLg/HF8HN6F2jsZ1ryyrfVYD2OlOnH51W9mlmVmked+XpFWzn2tJsZTPlnLmZ0XX79m3atm3LjRs3qFy5MidOnKBlS91UeU7fvu+Of6f1etjOteWVNa+QlJrE45jH1FtWj0VnFmV7npDYEOovq8+CUwsAWOO7Bo+fPbgZ8aLLOyAsAAkJRzNHHM0dc2xPhH0EW7duRd9Anw0bNvDBJx9ke+3Nfpup+3NdLodczuOzF3JDkiTi4uKKZZMkSWfPIygoiJCQEK36VsbGxnh7e3Pq1ClAXt8O5CAkODhYuQ1w584dtm3bxs6dO9m5cyc+Pj7MmTMnz+0wNTVl5MiRnDx5ktDQUABWrlzJl19+yXfffUdAQACzZs3iq6++Yt26dQA8f/6cXr16Ua9ePS5dusSMGTOYNGlSluefOHEis2fPJiAggPr16zN16lTWrFnDsmXL8PPzY9y4cbz99tv4+PgAEBwcjLe3Nw0bNuTChQvs3buXp0+fMnDgwDw/t4wGDRrEZ599hqenp9IrNGjQoAKftzgZFHcDBCE/bkbcZNetXZn2r7m8hv515W+LQc+CSExNxMTAhGq21ehWoxtLzi0hKS2JpDR57S1TA1PaVW2HnkoPUwNTElITAHi1+qvKOd1t3altX5vAiECtpRherfHimOxcu3aNLl268PTpU2rWrMmBAweoWrVqQZ56Jp2rdUZfpU9yWjLJadpDSicenOD8k/P4hfpxPfQ6v1z4hU9bfprleXbd3MW10Gssu7CMz1p/xopLKwgID2BrwFa+aCsnYudmNpuyWGmYP93e6Uat4bUIWBHA6p9W07FlR4YMGZLpMb/6/sqN8Bts9ttMwwoN8/EqCDmJj4/HwsKiWK4dGxuLubm5Ts4VEhICgLOzs9Z+Z2dn7t+/D4CjoxyQ29jYUKFCBa3j1Go1a9euxdLSEoB33nmHQ4cO8d132mv15UadOnUAuHfvHk5OTsyYMYMFCxYovVXu7u74+/uzfPlyhg4dyh9//IFKpWLlypWYmJjg4eHB48eP+eCDzF8Ivv32W3kRYuThv4ULF3L48GFatWoFQLVq1Thx4gTLly/H29ubZcuW0bhxY2bNmqWcY/Xq1bi6unLz5k1q1aqV7fOoXLmy1m3N66hhamqKhYUFBgYGmV7P0koEPkKppBl2auDcgM0DNnPhyQWGbB2SZbHCOg510NfT57WarxH8WbCS3AzgZO6EjYkNAMGfBfM07inG+sZaRQn1VHpcGXmF+9Ev/iAY6RtR1TrnAObq1at07NiRiIgIGjRowL59+zL9wdaFRhUb8fTzp0QkaM+gGrlzJEfuHZETtsNeDNMlpSZhbGCc6TyaY+5G3SU+JV55/bJM6s4ht0kTFD15/oRnic+IqBkBbYETMGLECGrWrEnz5trLe2h+bkU5LV8ovTLWuZIkKVe1r9zc3JSgB6BixYpKj01eaXqyVCoVYWFhPHz4kBEjRmgFMqmpqVhbWwNyLk39+vUxMTFR7s/4PtBo2rSp8n9/f38SExOVQEgjOTmZRo0aAXDx4kWOHDmSZXB7586dHAOf48ePa70mtra22R5bVojARyiVNB+QDSs0pJZ9LWxN5DfrvWf3iEuOw9zIPMt6MxUsKihJzhlZm1hjbWKd5X3GBsbUss/+j0dGV65coVOnTkRERNCsWTP27dtXqH9Q7M3ssTez19rXpGITOfAJexH4pElp3Iy4ST3nepnOoTlGQuLg3YOZZr+lPyan2WxWxlZUtqrMo5hH+NzzITQuFDqCxTMLYq/H0rdvX86fP4+Li7yu17PEZzx+/lg+v6j4XCjMzMyKbXadmZmZzs6l6XEICQmhYsWKyv7Q0NBcfanIOHtSpVLlO/csIEAeRndzc1POsXLlSlq0aKF1nL6+PpB1cJbdMGD6HjLNuXft2qW8ZzSMjY2VY3r16sXcuXMznSv965QVd3f3Mj11PSsi8BFKpYw9D47mjjiaORIWH8aN8Bs0qdSkwEUG8+vy5ct06tSJyMhImjdvzr59+4rlD0v6afgZaxBlFfikD3A2+21W/h8QFoBaUqOn0ssymMzy2o6ePIp5xF/+f8k79CC+dzwekgf+fv7069eP48ePY2xsrHXdu1F3SUhJwNTQNO9PWMiWSqXS2XBTcXJ3d6dChQocOHBA6e1ITk7Gx8dH60Pf0NCQtLS07E5TYAkJCaxYsYJ27dopQ2suLi7cvXs3y6FckIfG/vjjD5KSkpSA5cKFCy+9loeHB8bGxjx48ABvb+8sj2ncuDFbtmzBzc0NAwPdf6wbGRkV6utZ1ERys1AqZRXUZFy+oSBFBvPL19dXCXpatGjB/v37i+3blOb1OP/4PE/jnir7s+pViU6M5lHMI+X29sDtyv8TUhMIigoiLjlOqV79svpFmtc8/XnURmq+X/09dnZ2nD9/ngkTJgDaAZeExI3wG7l+jkLZEhsby+XLl5VZTkFBQVy+fJkHDx4AcgD36aefMmvWLP755x+uX7/OsGHDMDMzY/Dgwcp53NzcOHToECEhIURFRRW4XaGhoYSEhHDr1i02btxImzZtCA8PZ9myZcox06dPZ/bs2SxevJibN29y7do11qxZw8KFCwEYPHgwarWaDz/8kICAAPbt28f333+vPK/sWFpa8vnnnzNu3DjWrVvHnTt38PX15eeff1YSp8eMGUNkZCRvvfUW586d4+7du+zfv5/hw4frJGBxc3NTfhbh4eEkJSUV+JzFSQQ+QqmjVUcn3Qdw+gU709RpygdoXosM5telS5eUoKdly5bs27dPGd8vDprA53my9nTdrPJoMi7emtVjNK+nk7kTDmY5L6Cqec0znifGLIbffvsNkFd037JlS6ZATOT5lF8XLlygUaNGSm/O+PHjadSoEdOmTVOOmThxIp9++imjR4+madOmPH78mP3792vlqSxYsIADBw7g6uqqnKsgateuTaVKlWjSpAlz5syhc+fOXL9+HQ+PF1+83n//fVatWsXatWupV68e3t7erF27Fnd3dwCsrKzYsWMHly9fpmHDhnz55ZfK80qf95OVGTNmMG3aNGbPnk3dunXp1q0bO3bsUM5dqVIlTp48SVpaGt26dcPLy4tPPvkEa2tr9PQK/jH/+uuv8+qrr9KhQwccHR3ZsGFDgc9ZnFSSLucalgExMTFYW1sTHR2NlZVVcTdHyEJAWAAeSz0wNzQnZnKMMqV86fmljNk9hp61evJDtx+o+WNNTAxMiJ0ci76efqG26erVq7Rv356oqChatWrF3r17S8Tvj9siNyUp28LIgtjkWGrb1+bGR9q9Kr9e+pX3d7yvHKOhuT2702wqWVZi6LahtHdrz5GhR3K87tlHZ2n564sp+5rzfNXuK77t8C1ffPEFc+fOxcrKinpf1eNk3EnlmC/afMHszrN1+CqUL4mJiQQFBeHu7v7SD1SheP3xxx+89957REdHY2oqhndzI6ff79x+fpeqHp+XlTPPTSlzofRTFhHNUEcnfaFBTS+CZkZXYbpx4wZdunQhKiqKli1blpigB7SHAvvU7gO8mNmVnqaXRXNMxsekzxPKzdChppq0Ru/avbWuM2PGDNq0aUNMTAznFp+D1BfX0lTbFoSyZv369Zw4cYKgoCC2bdvGpEmTGDhwoAh6ilipCnxeVs68OEqZC0Uvu5lFmg/koGdBnH9yXmtfYQkKCqJz586EhobSqFEj9uzZU2KCHtB+/p2rdcbGxIY0KY3AiECt4zSvaXu39sowloGeAX3r9JXvTzclPjevqZWxFa5WL6pav1H3DeU8ICefbty4ETt7O1IepcABeMND+xhBKGtCQkJ4++23qVu3LuPGjWPAgAGsWLGiuJtV7pSqwCencua5KWUulA1KYrOD9mwtzcwuQJlNVJiBz6NHj+jUqROPHz/Gw8OjWBOZs5MxB0rzehy9d5SI+Bd1f5TlO9JVuK5lX0spJhgQHsC10GuZzpmba7vZuNHcRa5XcjvyNjfCb5CUmkTlypWZvGCyfPBZSL4pF1/U1BEShLJm4sSJ3Lt3Txmu+eGHH3Q63V/InVIV+OQkN6XMs5KUlERMTIzWJpRsypBLFh/Amn2aZRYKK7H56dOndO7cmaCgIKpXr87BgwdxcMg54bc4pA/86jrWVYa+Ptn7CU7fO3H20VmiE6OVOjoejh7KMR6OHrjbuGNiYEJiaiIPoh9kOmduru3p6Ekly0pYGVuRJqVR9+e61FtWjzR1GtZe1iCvAMK4UeOwlWzFzC5BEApVmQl8ciplrrkvK7Nnz8ba2lrZXF1zt+ikUDxS0lJeBDVZfACPaDQCe1N7LI0saVihId5Vs657URCRkZF07dqVwMBAXF1dOXTo0EuLhBUXzWvwXsP3sDCyYEi9IVSyrIShniFqSc2BuweUnCkXSxdsTGwYXG8wNexq8G79d9HX02dU01FYGVthaWTJW15vZSqUmJ1BnoOoaVeTYQ2HoVKp+KjZR1gZy8OAtyJvcSfqjtx71wVsXWx58uQJBnsMQBLDXbog5q0IZZEufq/LXAHDvJYynzx5MuPHj1dux8TEiOCnBNOsjG5hZKG1rITG2/Xf5u36bxfa9WNiYnj11Ve5evUqFSpU4NChQzpfe0uXDPUNOTrsqHLb282bx+MfM/fEXL449AV+YX5KJWtN71jbKm25NfaW8piF3RaysNvCPF+7mUszbo59scDpd52+47tO39FkRRMuBV/CP8xfDnyMYPTs0cx5bw5h58KgMvi3FQnO+aWpThwfHy+SZoUyJz5eHgbPWIU7L8pM4JPfUubGxsZKFU2h5NPk99R1qJurtXl0KT4+np49e3L+/Hns7e05ePAgNWvWLNI26Er6hUQrmMvvnYw5U4V2bUdPLgVfwi/UT+lt6tmhJ0bTjPj6669hF1zocAE6F0lzyhx9fX1sbGyUNajMzMyK/L0iCLomSRLx8fGEhoZiY2OjLAWSH2Um8MltKXOhdMspv6cwJSUl0b9/f44fP46VlRX79+/H07No26BLmjyeG+E3lFlcRfWaaq596tEpnjx/ouxrOqUpG7Zu4MaVG5xadgppTO4WnhQy03wRzO8CnIJQUtnY2BR4lfhSFfjExsZy+/Zt5bamhLadnR1VqlRRSpnXrFmTmjVrMmvWrEylzIXSrTiWoUhNTWXIkCHs27cPMzMz9uzZQ+PGjYvs+oXBzcYNM0Mz4lPiOX7/OFB0r6nmOgfuHACgslVlJfdn+crleLfyJt4/njXr1zB86PAiaVNZo1KpqFixIk5OTqSkpBR3cwRBJwwNDQvU06NRqgKfCxcu0KFDB+W2Jjdn6NChrF27lokTJ5KQkMDo0aOJiopS1kpKX8pcKN0yLk5a2NRqNe+//z5btmzByMiIf//9l9atWxfJtQuTnkqPug51uRh8kRS1/MFYVIu5anqWNNdN/7Ns16wdZp3NiN8Xz/hx4+nZvSdOTk5F0q6ySF9fXycfFIJQlpSqWV3t27dHkqRM29q1awH5W8706dMJDg4mMTERHx8fvLy8irfRgs6kn9FVFB/SkiTxySefsG7dOvT19dm0aROdO5edxJP0Q1suli5YmxTNumJuNm6YGrxIus0YxDZ5owk4Q3RUNGPHji2SNgmCUH6UqsBHKN9uRd7KcUaXrk2dOpWffvoJlUrF2rVr6du3b6FfsyilDziKMmdKT6WntaRFxmt7VfSCPqDSU7F58+ZMS9MIgiAUhAh8hFIjfXXhwk56nTNnDrNmzQJg6dKlvP124U2RLy5agU8R5ky97Nqejp5QCar3rA7A6NGjRWFRQRB0RgQ+QqlRVPk9S5cuZfJkeSmFefPmMXLkyEK9XnFJP1xYnIFPxgVNNT1Aqe1SqVGjBsHBwfI0d0EQBB0QgY9QaihrdBVifs/69esZM2YMIA91TZgwodCuVdyq2lTFwsgCKPryAJrrVbGuoszoUu77Lyi6H3ufBYsXALBkyRIuX75cpG0UBKFsEoGPUGoU9lT2rVu38t577wHw8ccf8+233xbKdUoKPZUeP3T7gY+afaQsIlpUulbvyohGI5jXeV6m+xzNHXEwc0BCwqWhCwMHDkStVjNq1CjUanWRtlMQhLJHJYkFXbTExMRgbW1NdHQ0VlZWL3+AUCSS05Ixn2VOqjqV+5/e13ly8759++jVqxcpKSm89957rFq1Cj098b2guLRf2x6f+z6s67uOTg6dqFOnDrGxsaxYsYIPPviguJsnCEIJlNvPb/GXXSgVbkfeJlWdiqWRJa5Wul1L7cSJE/Tr14+UlBQGDBjAypUrRdBTzDS9en6hfri4uDBjxgwAJk2aRFhYWHE2TRCEUk78dRdKhcKa0XXu3Dl69OhBQkIC3bt35/fffy+agm+SBMnJ8OwZhIXB06cQEgJPnsCjR/DwobyFhkJ0NCQlyY8pJ5S1xMLlhPaPPvqIBg0aEBUVxcSJE4uzaYIglHKlqnKzUH4VRmLzxYsX6dq1KzExMXh7e/P3339jZGSU9xM9fy4HKY8eycFLRASEh2v/GxkJcXHyFh8v/5uWlvdrGRmBiQlYWYGtLdjYyJvm//b2ULGi9ubkBAal662u+TlrAl4DAwOWLVtG69atWbt2LcOHD+eVV14pziYKglBKla6/hkK5pevEZl9fX7p06UJ0dDRt27Zl586dmJmZZX1wQgLcvg23bsnb7dsvemQePQJd1ZhRqUBPT95UKrmHJ+M6S8nJ8hYTI187N/T05ODH3R2qVcu8VaokH1OCaH7OQc+CiEuOw9zInFatWvH++++zatUqRo0aha+vL4aGhsXcUkEQShsR+AilglLDRwfTrq9evUqXLl2IioqiVatW7N69GwsLC7kXxs8PrlyBq1fB318OdB4+fPlJbWygcmW5h8XBQe55Sf+vnR1YWICZGZiba/9raCgHOllRq+VAJykJEhNf/BsTIw+TPXsGUVEv/g0Ph+BgeXvyRB5CU6vlnqiQEDh9OvM1zM2hbl3w9NTeqlTJvl2FzNHcEUczR8LiwwgID6BppaaAXFjyn3/+wc/Pj6VLl/LJJ58US/sEQSi9xKyuDMSsrpIn/YyuB58+wNU6/8nNfn5+tG/fnvDwcJrXrs3+11/HOjBQDnRu384+j8bGBmrWfLFVrQqurnKwU7myHNSURGlpcg7R48cQFAR372pv9+9DamrWj7W0hMaNoWnTF1v16kUWDHVY14Gj946yru863m3wrrJ/xYoV/O9//8Pa2ppbt27h6OhYJO0RBKFky+3nt+jxEUq8WxG3lBldla0q5/0Eqalw9Sr+27bRaf58whMTaQLsCwzE+r9lKRTOzlC/vrx5eUGtWnKg4+BQbL0fBaKvDxUqyFuTJpnvT02FO3fknq7r1+V//fzg5k05d8nHR940bGzk87RsCa+8Aq1ayflGhcDDwYOj944qeT4aI0aMYOnSpVy5coVp06axbNmyQrm+IAhlkwh8hBIvfWJzrmZ0paaCry8cPQpHjsCJE1x5/pzOQDjQENgP2FSrBi1ayB/kmmDH2bnQnkeJZGAAtWvLW//+L/anpEBAAFy8CBcuyP9eviwPqR06JG8g5wY1bCgHQZrNyUknTdMMa24L3EaapJ0I3u5/7bgy+gorVqxg5MiRNGjQQCfXFASh7BOBj1DivXSNLkmS83H27pUDnePHtRKOzwPdVCqiJIkmFSuyb8EC7Dp3BjFEkj1DwxfB4H/VrElJkXuDzp2Dkyfl1zkoCC5dkrfFi+XjGjSArl3lrW1beRZaPjSq0AiAmxE3WXB6Qab7vV/zxme3D59++imHDx8u9IVrBUEoG0TgI5R4yoyu9InNsbFyr8OePfL24IH2g6ytwdubk1Wr0n31ap7HxdGqVSv27NmDtbV1Eba+DDE0lHt3GjaEDz+U9z1+LAdAmu3aNTk5/MoVmD9fDnq8veUg6LXXoE6dXF+uZeWW/NLjF+5G3dXav+vWLvzC/Gg7oi1nD5/l6NGj/PPPP/RP32MlCIKQDZHcnIFIbi55PH72ICA8gCMd1tHeNwp27IBjx7SnepuYQPv20LkzdOgADRpw2MeHXr16ER8fT/v27dmxY4c8e0soPKGhckC6f7+8PXmifX+tWtCnj7y1bCnnIOXR10e+5ttj3zK84XAqXajEzJkzcXNzIyAgAJN89i4JglD65fbzWwQ+GYjAp2RJvuHH9LH16euvpnmGz1CqVZN7Ebp3l4OedHV49uzZQ//+/UlMTKRbt25s3bo1+zo9QuHQDEHu3/9iGDJ9sOroCL16yUFQt25gbJyr027228ygvwfRwqUFh946RO3atXn8+DHfffcdU6ZMKaQnIwhCSScCn3wSgU8JcPMmbNgAW7bIQyf/kVQqVG3bQr9+0KOHPNsqi7yO9evXM2LECFJTU+nTpw+bNm3COJcfqkIhiomRA6B//4Xdu+VEaQ1ra/nn+uab0KlTjpWm/UL98FrmhaWRJdFfRPPnn3/y9ttvY25uTmBgIC4uLoX/XARBKHFE4JNPIvApJk+fwsaN8McfcP68slttoM/BKmn4tnFn0rxT8rTsbEiSxNy5c5k8eTIAQ4YMYc2aNaK6b0mUkiIPV/77L2zdKucKaTg4wIABchDUtm2mqtIZ6zpVtqpMmzZtOH36NO+++y7r1q0r4icjCEJJIFZnF0q+2Fj4/Xd49VVwcYFPP5WDHn19efhq7Vrm/T2ebu/Czdc75Bj0pKWlMXbsWCXomTBhAuvXrxdBT0llaCj37CxZIiemHzsGo0bJQU94OCxbJidFu7vDtGlyscX/GOkbUcu+FiAnvqtUKhYtWgTAb7/9hq+vb3E8I0EQSgkR+AhFS5Lk4ObDD+XlHd55B/btkysMN28ufxA+eSIPhQwdyoUk+QMvp6UqEhISGDhwID///LPyIThv3jz0Stj6U0I29PTk+j9Ll8pLbezbJ0+ht7aWg6IZM+SK0R06wPr1EBeXaRHT5s2b8+abbyJJEp9//jmiI1sQhOyITwahaDx7Bj//DI0ayQHOypVyj0/16vD113Jez9mzMHasVgG8ly1OGh4eTteuXdm6dStGRkZs3LhRrN9UmhkYyFPfV6+W1xbbsEG+rVLJBSmHDoWKFfli3R2aPH7x+wEwa9YsjIyMOHz4MHv27Cm+5yAIQokmcnwyEDk+Onb2rPxN/q+/5FXOQZ6988Yb8MEH0K5dtktBJKclY/adGWlSGg/HPcy0XIWfnx+9evUiKCgIa2trtm3bRvv27Qv5CQnF4sEDubdn7Vp5iY3/XHc3x+vrn2HQIDAxYcKECXz//fd4eHhw5coVDHJIkhYEoWwROT5Crvxw+gc+2/eZbocGkpPlJOUWLeRaLevXy0GPl5dc3ffJE2JX/8Lg8OW0X9eB7n90xzc4c17GzYibpElpWBlb4WKpPVNnx44dtGzZkqCgINzd3Tlx4oQIesqyKlVg6lS4dQt8fHj2ek+S9MErKA6GDZMXip04kSlDhmBnZ4e/vz9r1qwp7lYLglACicCnHEtMTWTCgQksPLOQgPCAgp8wJAS++UZeufztt+WlDYyM5OGJ06flFdA//hjs7NgeuJ0N1zfgc9+Hvbf3sujsokyn0+RvpF+jS61WM2vWLPr06UNsbCzt27fn/PnzeHl5Fbz9QsmnUkG7dphv2or7Z/p80QlSK7tARATMn49t48ZM+y8J/quvviI2NraYGywIQkkjAp9y7Eb4DWXxx4wrYOfJlStyknKVKjB9uhwAVaoEM2fCw4fy8ETLllpDWtdDrwNQwaJCttfPmN8TGRlJnz59+PLLL5EkiVGjRrF//37s7e3z33ahVDLUN8S2am3mvgIHDvwC27fLRRAliVH+/lQHnj59yvyhQ7WLJgqCUO6JwKccSx9spE8SzRVJAh8fedp5w4bytPSUFGjdWq7Hc+8efPlltit1a643wGMAAAHhAagltdYx6RcnvXDhAo0bN2bnzp0YGxuzcuVKli5dKqarl2OagNgv8oZcAXrvXrh5E6MxY5hjZATA91u38sTNDRYu1Fq4VhCE8ksEPuVY+mAn14GPWg3//AOtWsnLROzdK09HHjToxardgwbJdVpyuvZ/QVevWr0w0jciPiWee8/uZW6fGgJ3BtKmTRvu379P9erVOXPmDO+//35enqpQBimBT/rf3Zo14aefeP3xY1q5uhIPTHvyBD77DFxd4YsvICyseBosCEKJIAKfckzTo5Lx/1lKSZGHrDw9oX9/ebaWsTGMHClPRd+4EZo1y9V1E1ISlBW36zvXp45DnUxtSEpN4ubdm/AbLJ+1nOTkZPr168fFixdp2LBhnp6nUDZpajtl9burcnBgwaZNAKxWqbjq5ib3+MydC25uciAUHFyErRUEoaQQgU85lv6b8s2ImySnJWc+KDVVDnjq1pWLyt24IReWmzwZ7t+XK+xWr56n694Iv4GEhL2pPU7mTi++uf/XCyRJEkt+XYJ6qRqCwMzMjF9++YUtW7ZgbW2d7+crlC2a3xv/MP8sZyW2atWKAQMGIEkSE2vVkpfHaNYM4uPloS93d7lu1MOHRd10QRCKkQh8yqmElATuRMr1UAz1DElVp3Ir4taLA1JT5WnomoDnzh15Ne25c+WaKrNmgbNzvq6tJC07eaJSqV5U4Q3z4+HDh/Tt25eJIydCIpi7mePr68v//vc/ZWaXIADUsKuBoZ4hscmxPIh+kOUxs2fPxtDQkH3797PfxETuqdy7F9q0gaQk+OknOXD/3//k32tBEMo8EfiUU5peFztTOxpXbAz8F5CkpcmJyh4e8jT027fl9ZPmzYOgIJg4EQpY2FHTs6P5xu7p6AlqOLL5CB4eHmzfvh09Az3whkELBlGrVq2CPVmhTDLUN9Rasysr1atXZ8yYMYC8fluaWi3P/jp+HA4flvPUUlJgxQo5P+jTTyE0tIiegSAIxUEEPuVU+hlTno6eIEHaP1uhXj15avqtW2BvD3PmyAHPhAlgbq6Ta2s+pDQ9PQl3EmAlPNr4iNjYWFq1akXHOR2hA3hVFPV5hOxp8nxyKscwdepUbGxsuHr1Kr///ru8U6WS1/46ckQOgjp0kAtvLl4M1arBV19BdHRRPAVBEIqYCHzKqfQ1crqEmHF8Nbz11SYICAA7O3koKygIJk0CCwudXlsTdNkl2jFw4ECG9BoCwYAxfDP/G06cOMFDYznvIqfFSQVByfMJzz45397enilTpgByEJSgWTpFo21buffnwAE5ByguTq5B5e4u93TGxxda+wVBKHoi8Cmn/ML8qBMGE+af5M2RP9H2ISQYqmDKFLh7V05etrQEYPi/w2m7ui1JqUnce3aP2j/VZvGZxbm+1rLzy7CZY4PFLAssZllw5/4d2A3vdXmPv/76Cz09PexfsYex0KhXI1LUKdyOvA1kvzipIMCL34/1V9ZjN9eONb5ZL1MxduxYqlSpwqNHj1i8OJvf3c6d5Rygf/6Rh3qjouTAv0YNedHUtLTCehqCIBQhEfiUR0+eMHjJEa4vhWrHriHp6bGyMdT5RI/kb7+WZ239Jy45jjWX13Dy4Ul8Q3zZHridmxE3WeW7KteXW3lpJdFJ0cRFxhG3PQ4WA+cgOTmZTp064evrS9dPuoKFHJBp1uiyNramkmWlQngBhLKiTZU2WBlboZbURCVGseZy1oGPiYkJ3333HSAnPIeHh2d9QpUK+vaVl1dZv16e+h4cDCNGQJMmcOhQ4TwRQRCKjAh8ypPERJg1C6lWLQadfo6+BIk9u8O1a3w+wIoHFmnaM7tAaw0v/zB/JZciMDyQlLSXLwWQpk7D76ofbAWDJQZwFkiDNm3bcOjQIQ4cOED9+vW1itFlnPUlCNmpYFGBx+Mfs2fIHkD+/cluwd3BgwfTsGFDYmJimDlzZs4n1teXc90CA+Wp7zY28tIsnTvLVaJv3NDxMxEEoaiIwKc8kKQX3fdffokqLo7TleG1UVYYb9+FysNDa0p5elrLWoS+CEpS1CncibqT7SUTEhLYsGEDrdu1JnlpMlyF1JRU2rZty4EDBzh+7DgdO3ZUApv0SarK4qQOHrp7DYQyy8LIAu+q3uip9IhMiORp3NMsj9PT02P+/PkALF26lDt3sv/9VRgZwbhx8uzGsWPlgGjnTvDykm9n13MkCEKJJQKfsu7aNflbav/+crJypUqcmDWS1iMgtlmDF4GHY9azY9JXxfUL89O+neFYtVrNmTNnGDVqFBUrVmTw4MGcO3kO9MCmqQ3nzp3j+PHjdO7cOVNPjub6AeEBXAu9Ju8Tic1CLpkamlLNthqQcxXyzp07061bN1JSUpSE51yxt4clS+D6dbnHJy1NrgFUsyYsXSryfwShFBGBT1kVEQFjxsgLiB4+LC8vMXUqBAayq5kNqLQTh7Nc9yjD7ZMPTxKVGKV1X2pqKkePHlWSR1u1asUvv/xCdHQ0VatWpeOwjvAJvDb5NZrlsKRFNdtqGOsbk5iayKGgQ1ptEoTcyC54z2jevHmoVCo2b97M2bNn83aROnXkleAPHoT69eHZM/l91qwZnD6dz5YLglCUROBT1kiSvMREnTryN1G1Gt54Q85JmDEDLCy0cmg0NENdGb8tpw98YpNjQQ2EAmdg+WfLsbOzo0OHDvz00088fvwYCwsL3n77bQ4ePMjdu3ep1LsSWL88iNHX01fW7IpNjs3UPkF4meyC94zq16/P0KFDAZg4cWK2OUE56tQJLl2Se32srcHXF1q3huHDRQFEQSjhROBTlvj5gbe3vMREeLich3DkCPz1lzw75T/pixdqaIKMW5G3lDW7ouKiuHfrHgSAkY8RrAfmAkuBvfDk4hOeP3+Ovb097733Hjt37iQsLIzffvuNTp06oaenl6lKc07SBzrWxtZUtKhYoJdDKF+UPLGXBD4AM2bMwMTEhGPHjrFjx478XVBfX+7tuXlTfs8BrFkDtWvDzz+L4S9BKKHKVOAzffp0VCqV1lahQoXiblbhi4+X6+40bChXoTUzkwuvXbokl+RPf2hKvLIyel2HuoSHh3P58mUuH7uMyWUTUg+k0rNfTzw9PXGydYKfgU2QfCQZ7gJJoG+kD9VAr4seZ8+dJTQ0lNWrV9OjRw9MTEyUa6Wp05RZYZoepZxkDMTEjC4hL5QE/dDsZ3ZpVK5cmXHjxgEwadIkUlNT839hJye5zs+pU9CokTz89dFH0Ly5/B4UBKFEMSjuBuiap6cnBw8eVG7r6+sXY2u0paWlceLECSRJ0trUanWmfdntz7TvwgXUq1cjhYeTBCTWr09Cnz4kpqaSOHMmCQkJJCYmEh0dTVRUFA+fPkQKktBL0sNtthtJSUmZ2nmAAy9uGIJlZUsq16hMgFEAVIZfP/iVj/Z/RGxyLJZulujpZR0/Bz0LIjE1ERMDEyXxNCdZ5RwJQm7VcaiDnkqPqMQoQmJDqGiZc4/hpEmTWLFiBTdu3GD16tV8+OGHBWtAq1Zw/jwsXw5ffikHPc2by7PCvvlG/kIiCEKxK3OBj4GBQYnt5UlMTKR9hh4Ynbt6Vd5eQo2aJOSgx8nJicqVKxOuH84D6QFtG7blnc7vcDTuKBvub+DdFu/SwLkBH+6UPxgaujSkrkNdzj85j1+YH3Ud6yrnfZ70nEcxjwDwue8DyB9I+novD0DTD3WJwEfIKxMDE6rbVudW5C3239lPz1o9sTezz/LYyIRI7KztmDZtGp988gnTpk1j8ODBWBR0eRZ9fZ6PeAfjvr0w+mwibNwI338PW7bIAVGXLsCL94m+nj417Gqgp9JDkiTuRN0hJS2FSpaVsDaxfsnFBEHIjzIX+Ny6dYtKlSphbGxMixYtmDVrFtWqZd/bkJSUpNXrERMTU2ht09fXp06dOpmG41QqFXp6elnuz/K+sDBUQUGoUlPRA1Surqjc3TE2M8PU1BQTExPlX81mZWWFra0tex7uYdv9bbzR5A3m95lPxYoVMTY2BuCH0z8wfv94TnCCEzdOyI3W+28h0/+CEj2VHrUdauPp5Mn5J+e1kqFjkmKovqQ64fHatU1yM8wF4G7jjomBCYmpibl+jCCk5+Howa3IWwz7dxiGeoZcH31dWcFd48ezP/Lx3o/Z8PoGRo4cyZIlS7hz5w4LFizg66+/LtD1oxOjqbakGjXsanB2w1kOtq5AramLqBIUBF27wjvvEDv7W6r/2Zyw+DAARjQawareq/h8/+csPLMQACtjK+58fAcHM4cCtUcQhMzKVODTokUL1q9fT61atXj69CkzZ86kdevW+Pn5YW+f9Te/2bNn88033xRJ+0xMTAgICHj5gdl5+BBGjpRzCUDO6Vm9Ws4ryKWdG3aCAXi38sYtXcIzQL+6/Vh+cbnyBxnA2dyZ3rV742juSHu39ng6emJiYJLlDJpLwZcIjw9HX6WvfFs1MzRjaIOhuWqbvp4+Y5uP5eTDk7Sp0ibXz0kQNEY0GsGFJxcIjQslRZ3CkaAjmQKfvXf2ArD/zn7e9HqT2bNnM3DgQObPn8///ve/AvUYX3hygciESM49PsezxGf8WimEnWNg4+Xq9Nh7F377DaNd2+nSPpo/6wEquR3p2wXyl4gzj87Qs1bPfLdFEIRsSGVYbGys5OzsLC1YsCDbYxITE6Xo6Ghle/jwoQRI0dHRRdjSl0hLk6RffpEkS0tJAkkyMpKk776TpOTkPJ+q2uJqEtORDt89XKAm7b65W2I6kufPnsq+n8/9LDEdqeefPQt0bkEoqM/2fSYxHenj3R9nus9tkZvEdKQWK1tIkiRJarVaat68uQRII0eOLNB1l5xZIjEdielIJx+clOovqy8xHanuT3Ul6cwZSapXT34Pg3SkkZ3k9Ll8bHhcuGTwrYHEdKTWv7aWmI4098TcArVFEMqb6OjoXH1+l6lZXRmZm5tTr149bt26le0xxsbGWFlZaW0lSlCQXHl55Eh4/hxatoTLl+VV1A0N83Sq+JR4gqKCgILXyNE8/mbETWXNLrHUhFBSZLcES2xyLPee3QPksg6SJKFSqZSlLFauXMmNAqzDlf56V59e5Ua4fK5bkbdIbtoILl5k59stSNaD9r6RBCxV8YYfbA/cTqo6FQsjC7pV75Zl2wVB0I0yHfgkJSUREBBAxYqlsB6MJMGvv8rVYY8cAVNT+OEHOHEC6tZ9+eOzcCP8BhISDmYOOJk7Fah5rlauWBhZkKJO4VakHFhmVRhREIpDdsUMA8JeDDU/T37Ow5iHALRr147evXuTlpbGF198ke/rpr/e9sDtSk2sVHUqNyNugqEhCzub0exDiKzpil28xF9/QbXRU7CPkwM2Lycv+VwvqUAtCEL+lKnA5/PPP8fHx4egoCDOnj3LG2+8QUxMjFKltdR4+hT69IH334fYWGjbVl5z69NP5aJp+ZSXYoIvo1KpMlV7zqowoiAUB83vZkhsCJEJkcr+nBbhnTt3Lvr6+vz7778cP348z9eUJEnrfAfuHtC6X/P+8Avz42oFuLtvI/veak6qCrzPhOC3FIbctdBat04tqfPcDkEQclamAp9Hjx7x1ltvUbt2bfr374+RkRFnzpyhatWqxd203Nu2DerVgx075JWh582Do0ehevUCn1rzR19XM6bSr40UFhdGWHwYKlRa09sFoThYGltSxboKQI4L66a/r06dOrz//vsATJgwIc9LWTyNe6q1ll2qWrsool+oH+Hx4YTGyUta1HGpz4PP3qfl++DnCM5x8PHsw9QcPxP7FEPiU+K5/+x+ntogCMLLlanAZ+PGjTx58oTk5GQeP37Mli1b8PAoJfkmMTFy2ft+/SAsTB7iOn8eJkwoUC9PespQlI56ZNIPJ2jO7W7rjpmhKNQmFL/0lZw1NL+nlSwrad3WmD59Oubm5pw9e5a///47T9fTXCfjUivpr6U5xs3GDQsjCzwcPbjoAk0+hDltQNLTQ++337n8i4qWD0WejyAUhjIV+JRax4/Lgc7ataBSwaRJcO6cvE+HlKEuHeXgpF8bSZfDaIKgC1nl+Wh6eN6o+0am+wAqVKjAhAkTAJg8eTLJycm5vp7mXC0qt8DVylXZn/5aGYeDNe+hJEOY3AVCd/8FVatSOSKZ46vBeu5iKMhyGoIgZCICn+KUliaXsm/fHu7fB3d3OHYM5syB/4oK6kpccpwym0VXwYnmG/XNiJtceXpFa58gFLeMgU9sciz3o+Whozc85GBEM7Mrvc8++wxnZ2fu3LnD8uXLc3299LMa078PBngOAOBWxC18Q3zlY/6738bERukRsjSyxKlrP7hyheud62MgwSurD8oLDwcF5e3JC4KQLRH4FJdHj6BjR5g+HdRqGDoUrlyRE5nzSC2pOXj3IM8Sn2V5/5lHZ1hydokyo8vR3LFgbf+Pq5UrlkaWpKpT2R64HRA9PkLJoelNyZh872zuTMvKLTHUMyQ2OVaZ2aVhYWGhFDX95ptviI6OztX1/MP9letq3gdmhma0dm2NlbEVaVIa/wb+Kx+T7n2iCYI8HD3khXmtrbm1ZDpD+kOsiZ5csLRhQ/jjj3y8CoIgZCQCn+KwfTs0aCD37lhYwO+/y8Nclpb5Ot0W/y10+a0L4/aNy3Tf3ai7tFndhimHpwAoU2V1QaVSKR8uT+Oe6vz8glAQdR3kJHvNzK70Q72G+oZKReespo2PGDGCOnXqEBERwdy5c196rfQzujwdPZX3gaejJ3oqPeW2JrE5/fvEy9Er0z5PJ0/+rA/NxxghtW4t5wC+/ba8xcbm7YUQBEGLCHyKUmIijB0rT1WPjIQmTcDXF4YMKdBpzzw6A8DZR2cz3XfhyQXUkhp7U3t61urJdO/pBbpWRjM7zKRHzR50q96Nz1t9TsMKDXV6fkHIr/Qzu/xC/TIl96fPUcvIwMBACXh++OEHHj58mOmY9EJiQ4hKjFLWsnvD4w2GNxzOdx2/A2BGhxnK++SzVp/RuGJj5bEfNf+Idxu8y7iWL764VLOthpG+EQGWiQRtWyP3DOvpyb0+TZvmaiFiQRCyVqbW6irRbtyAN9+Uh7MAPvsMZs2Sp6wXkOYP963IWySnJWOk/+Kcmm+hfev0ZVXvVQW+VkadqnWiU7VOOj+vIOiCp6MnD6IfZJlYrKkwnt3MqV69evHKK69w/PhxJk+ezO+//57tdTTnqG5bHRMDE0wMTPi1z6/K/R3dO9LRvWOWj61uV511fddp7TPQM6COQx2uPr2KX2Qg1b7+Gjp1kv+GBAZCixbw448wYoQ8IUIQhFwTPT5FITJS/kN15Qo4OsLu3fD99zoJeuDFH12lOmwW94mkY6E80gQ5/mH+md4LSo9PNhWSVSoVP/zwAyqVij/++IMzZ85kex0lqNJh1fJMs9LatpWXq3n1Vbn3+IMP4N13xdCXIOSRCHyKgp2d3MPTqZMc/HTvrrNTRydG8yjmkXI7uwJtIulYKI80Qc6ZR2d4EP0AeBGcpA+KsitW2KRJE4YNGwbAp59+ilqddSXlwijnkL59CgcH2LULZs+W63v9/js0awbXr+vsuoJQ1onAp6h8+SXs2wc6XjcsIDxA63b6bvvktGRlHS2xfpZQHml+788/OQ9ABYsK2JnaAVDDrgaGeobEpcQpQVFWvvvuOywsLDh79iwbNmzI8hhdFweF7BdaRU8PvvhCXsPPxUUeRm/eHNas0dm1BaEsE4FPUdHX11kF5vRyKsF/M+ImqepUrIytcLF00fm1BaGkyzjEmz4w0ZrZlUOF5IoVKzJlijwrctKkScTFxWndL0lSoSzQqzlXQFg2a3a98oo8OeLVVyEhAYYPh//9D5KSdNYGQSiLROBTymn+4Gqm7qb/A56++10lEiCFcsjCyIKq1i/W6svYI/OyPB+NcePG4e7uzuPHj5k3b57WfSGxITxLfIaeSk8JpHShum11jPWNSUhNICgqmwKGjo7y0NfMmXKS84oV0K6dXCdMEIQsicCnlNMEOgM9BwJyddikVPkbn6b3RyQ2C+VZ+l6Y7HqANMUHs2NiYsL8+fMBmDdvHg8evBga07wHa9jVwMTARCdtBtDX06eOQx25fWE5tE9PTx5K37MHbG3l5W6aNAEfH521RRDKEhH4lHKaP4hdq3dVqsNqZnYVRt6BIJQ2mmnrkHkoSpk59ZIeH4D+/fvTrl07EhMT+eKLL5T9hblOXU61hjLp1g0uXJCLo4aGypMpFi2CPK4yLwhlnQh8SrH0M7o8HD2UP7xfHv6ST/Z8wvEHxwGR2CyUb+l//zMGJ5oeIP8wf9SSmsTUROacmJNlD4tKpWLRokWoVCo2bNjAqVOngML9gpGx1lBgeCBzTswhPiU+6wdUqyYvcTFkiLwW4LhxcrXnDHlJglCeicCnFNP8cXaxdMHGxIZGFRoBsOPmDpacW0JoXCh6Kj3qO+t2lXdBKE0074uq1lWxNbXVui/jzK5N1zcx+dBkPtv/WdbnatSIESNGAPDxxx+TlpZWqEPKGXOQJh6cyORDk/njag7rdpmZwW+/wZIlYGAAf/4JrVvDvXs6b58glEaicnMplrEg2zTvaVSyrERCaoJyTJOKTahgUaFY2icIJUGDCg34rd9vWSYeG+obUtuhNtdDr+Mf5s/lkMsAXAm5ku35Zs6cyV9//cXFixdZvnw5fs91P6NLQ9OLFBAeQJo67UX7nmbfPkBOdB47Vh72GjhQXuKiWTPYulWeDSYI5ZgIfEqxjMUJnS2c+bLdl8XZJEEokd6u/3a293k6enI99LrWel7BscFEJURl6iECcHZ2ZubMmYwdO5YpX04h+oNo9C30qW1fW+ftrmZbDWN9YxJTE7kWek2pN5SrnB+QZ3hduCCvD3jpkpz3s2yZvNSFIJRTYqirFCuM2iGCUN6kXxpCqxxEDsHFqFGjaNSoEdHPouGAPGRmbGCs87aln9n1t//fyv4cZ3llVLkyHD8OAwZASgq8/z6MHw+pqbpuriCUCiLwKcUKczaJIJQXmqHikw9P8uT5E2V/TsGFvr4+S5culW9cBudI50Jrn+aLzWa/zcq+0LhQwuPDc38SMzPYtAm++Ua+/cMP0KsXREfrsqmCUCqIwKeUepb4jMfPHwNQ17FuMbdGEEovTWBxO/K21v6XTXFv2bIltbvIw1uB6wNJLaQeFM0XG83yM7ltXyYqFUybBps3g6kp7N0LLVvC7dsvf6wglCEi8CmlAsLkNbo0M7oEQcifGnY1MNI3yrQ/N3k0Vj2swASe3nn6ogdIx7Lr0c11nk9GAwbAiRPa63wdPZr/BgpCKSMCn1JK5PcIgm4Y6BloJSa3cW0DvDywkCSJmwk3obN8+6uvviIkJETn7cv4Hte0L095Phk1bgznz0OLFhAVBV27yiu9C0I5IAKfUkrk9wiC7qSvwfOGxxuAvAZXZEJkto8Jjg0mOikavSZ6NG3alJiYGCZMmKDztrnbuGsthTHAYwBQgB4fjYoV5RXeNUnP77wDM2aISs9CmScCn1JKLEchCLqT/n3UsnJLqlhXAXLuVdF8+ajpUJNly5ahUqn4/fffOXTokE7bln5mV0WLirSt0lbr+gViagobN4ImYJs2TZ71lZJS8HMLQgklAp9SSixAKgi6k3EhU837KqfgIv1wc9OmTRk9ejQA//vf/4iPz2ZJify277/AzNPJk7qOdVGhIiw+jNa/tmbmsZkFO7meHsybB0uXyv9fvRp69BAzvoQySwQ+pVD6GV0i8BGEgmtWqRkGegZ4OXlhZWylVdsnOxmHm2fNmkXlypW5c+cO32imjeuIJq+ndeXWmBma0aiivAzH6Uen+erIV8QkxRT8IqNGwfbtYG4OBw7IFZ4fPiz4eQWhhClw4JOYmKiLdgh5oOntqWxVGWsT62JujSCUfq7Wrlz+32UOvnMQeBHM5DTU5R+uXTndysqKZcuWAbBgwQIuXbqks/Z90OQDTg0/xZRXpgCwZ8getg3ahpO500vbmSc9esCxY3L+z7Vr8nT3y5d1c25BKCHyFfio1WpmzJiBi4sLFhYW3L17F5BnNfz66686baCQmUhsFgTd83TyxNnCWfk/ZN/jI0mS8j5M3+vas2dPBg4cSFpaGu+//77OavsY6BnQyrWVUh3aydyJPnX6KAsQ6yTfR6NxYzhzBjw94ckTedmLw4d1d35BKGb5CnxmzpzJ2rVrmTdvHkZGL+pf1KtXj1WrVumscULWRH6PIBSuug5yUdDsZnY9ef6E6KRo9FX6mRY/XbJkCba2tvj6+rJw4cJCbWduhuTypUoVOHkSOnSA58+he3f4+++XP04QSoF8BT7r169nxYoVDBkyBH19fWV//fr1uXHjhs4aJ2RNzOgShMJlaWypzOzKqjdF8x6saV8z0xpdzs7OLFiwAIBp06bh76+jYags5GZILt+srWH3bnj9dUhOlld5/28oTxBKs3wFPo8fP6ZGjRqZ9qvValLENMhCJ4oXCkLhyymo0OzL7svHsGHD6N69O0lJSQwdOrTQlrNQZp/pusdHw8REXuNr5Ei5vs/o0TB9uqj1I5Rq+Qp8PD09OX78eKb9f/31F40aNSpwo4TsPUt8piykKIa6BKHw5DSMlFV+T3oqlYqVK1diY2PDhQsXmDNnTuG08b8vP49iHhGdWEjTz/X15anu06fLt7/5Rg6A0tIK53qCUMjyFfh8/fXXfPTRR8ydOxe1Ws3WrVv54IMPmDVrFtOmTdN1G4V00s/osjK2KubWCELZlVOCc26Gm11cXPjxxx8B+Pbbb7lcCLOjbExsqGRZCSik4S4NlQq+/loOgFQq+OUXeehLzOoVSqF8BT69evVi06ZN7N69G5VKxbRp0wgICGDHjh106dJF120U0hEzugShaGRXxFCSpFwPNw8ZMoR+/fqRkpLC0KFDSUpK0nk7Cy3BOSujRsmruxsZwdatctJzjA5qCAlCEcp3HZ9u3brh4+NDbGws8fHxnDhxgq5du+qybWVWQYqNicRmQSgamsDnadxTIuIjlP1Pnj8hJikGAz2DTDO6MlKpVPzyyy84ODhw9epVvvrqK523s1ATnLPyxhuwZw9YWsqrunfuDJHZr2kmCCVNvgKf8+fPc/bs2Uz7z549y4ULFwrcqLLqUcwjnL93puKCiqgldb7OIRKbBaFoWBhZUNW6KiAHFQkpCTRZ0QS3xW4A1LSriZG+UQ5nkDk5OSllPubPn8/+/ft12s5CT3DOSseO8gKn9vbyKu/e3lAIK9MLQmHIV+AzZswYHmZRyvzx48eMGTOmwI0qqypYVOBZ4jPiU+K59+xevs7xstkkgiDoTvo8n3OPz3Ep+BKpanmGVs9aPXN9nj59+jBq1CgA3n33XUJDQ3XfRl0WMcyNJk3Ax0eu8nz9ulzo8MGDom2DIORDvgIff39/GjdunGl/o0aNCrVmRWlnoGegrLKcn27p9DO66jrW1WnbBEHIzMPhRZ6Ppkelc7XOBH8WzLwu8/J0rgULFuDp6cnTp08ZNmwYanX+en0ztfG/Hp/Hzx/zLPGZTs6Za56e8hIXVavCrVvy+l63bxdtGwQhj/IV+BgbG/P06dNM+4ODgzEwMChwo8oyJRExH9/ONI9xtXIVM7oEoQik7/HRfFlpXKExFSwq5PlcpqambNy4ERMTE/bs2cPixYt10kYbExtcLF2AIszzSa9GDTh+HGrVknt8XnlF7gEShBIqX4FPly5dmDx5MtHRL+pGPHv2jClTpohZXS9RkBkYIr9HEIpW+sRhzfuvIPWzvLy8lGUsJk2axOnTpwveSF78TSiWwAfA1VXu+alfX8718fYGke8plFD5CnwWLFjAw4cPqVq1Kh06dKBDhw64u7sTEhKilGoXslaQRESR3yMIRUszpPw07innH58HCv7FY+TIkbzxxhukpKTwxhtvZNl7nlfph+SKjbOznPDcvLk8y6tjRzhxovjaIwjZyFfg4+LiwtWrV5k3bx4eHh40adKExYsXc+3aNVxdXXXdxjJF80czICwgzzO7dPGNUxCE3Es/sysuJQ54sYBpfqlUKlavXk3dunV58uQJgwYNKvCSFi9bTb7I2NnBwYPQvr28uOmrr8o9QYJQguQ7Icfc3JwPP/xQl20pF6rbVsdY35iE1ATuPbtHNdtquX6sKF4oCEXP08mT+9H3AXC3ccfcyLzA57S0tGTr1q00a9YMHx8fvvjiC77//vv8t7Eoixi+jKWlvLhp376wf79c5HDXLjkYEoQSIN8FDG/evMmKFSuYOXMm3377rdZW3JYuXYq7uzsmJiY0adIky3XFiou+nr4ysysv3dJRCVEExwYDosdHEIpS+i8ausyvq1OnDmvXrgXk9IGNGzfm+1yavwlPnj8p+pldWTE1hX//lXt84uPhtdfg8OHibpUgAPkMfFauXImHhwfTpk3j77//5p9//lG2bdu26biJebNp0yY+/fRTvvzyS3x9fXnllVfo3r07D0pQfYn85Plo8nuqWFfB0tiyUNolCEJmWoGPjntbX3/9dSZOnAjIK7qfOXMmX+exNrGmslVloBgTnDMyMYF//pGDnoQE6NFDHgYThGKWr8Bn5syZfPfdd4SEhHD58mV8fX2V7dKlS7puY54sXLiQESNG8P7771O3bl0WLVqEq6sry5YtK9Z2pZfbbunw+HCuPr3K1adXORR0CBC9PYJQ1NK/5wrj/Tdr1ix69epFUlISvXv3JigoKF/n0bTt4N2DPIzOXGC2WJiYyGt69eghL2jaq5c8/CUIxShfgU9UVBQDBgzQdVsKLDk5mYsXL2ZaM6xr166cOnUqy8ckJSURExOjtRW23Ew9fRTzCNcfXGnwSwMa/NKAr49+LT9W5PcIQpFKXyy0MN5/+vr6/PnnnzRq1IiwsDB69OjBs2fP8nweTdu+Pvo1VRZV4cCdAzpuaT4ZG8OWLXLQk5gIvXvD3r3F3SqhHMtX4DNgwACdrzejC+Hh4aSlpeHs7Ky139nZmZBs1pGZPXs21tbWylYUs9I0f6Bymtl14sEJElMTMdI3wtncGWdzZ2rZ12JwvcGF3j5BEF6wMLJgTLMxdK/RnQYVGhTONSws2LFjBy4uLgQEBDBgwACSk5PzdI4h9YZQy74WZoZmAEovcYlgbAx//w19+kBSkvzv7t3F3SqhnMrXrK4aNWrw1VdfcebMGerVq4ehoaHW/R9//LFOGpdfKpVK67YkSZn2aUyePJnx48crt2NiYgo9+KlmW02Z2RUUFUR1u+qZjtH0Br1b/11W9l5ZqO0RBCFnP732U6Ffw8XFhZ07d9K2bVsOHjzI22+/zYYNG9DX18/V45tUakLgR4EsPb+UMbvHlIwZXukZGcHmzfDmm3LuT79+LxKgBaEI5SvwWbFiBRYWFvj4+ODj46N1n0qlKrbAx8HBAX19/Uy9O6GhoZl6gTSMjY0xNjYuiuYpNDO7rjy9gl+YX5aBj6jZIwjlT8OGDdm6dSs9e/bkr7/+wsLCglWrVqGnl/vOeWXyRHEWM8yOkRFs2gRvvSUPf/XrJ09179ixuFsmlCP5GuoKCgrKdrt7966u25hrRkZGNGnShAMHtMe2Dxw4QOvWrYupVVl7WZ6PUrNHLE8hCOVK165d2bhxI3p6eqxZs4Zx48YhSVKuH68ZSg96FkRcclxhNTP/DA1hwwY510eT8CwqPAtFKN91fEqq8ePHs2rVKlavXk1AQADjxo3jwYMHjBw5sribpiWnmV1JqUncjrytdZwgCOVH//79WbNmDQBLlixhypQpuQ5+HM0dcTRzBOBG+I1Ca2OBGBrKw17p6/ycPVvcrRLKiXxXbn706BHbt2/nwYMHmZLwNIvwFYdBgwYRERHBt99+S3BwMF5eXuzevZuqVasWW5uyklN3dGBEIGlSGtbG1lSyrFTUTRMEoQR49913iY2NZcyYMcyZM4e4uDgWLVqUq2EvD0cPfO774BfmR5NKTYqgtflgbPxiqvuRI9Ctm1zksHHj4m6ZUMblK/A5dOgQvXv3xt3dncDAQLy8vLh37x6SJNG4BPzSjh49mtGjRxd3M3KkzOwKDyBNnYa+3osERmUxUifPbJOyBUEo+zR/x8aMGcOPP/7I8+fPWblyJQYGOf/p9nT0lAOfkpjnk56pKezYIff8nDgBXbrA0aNQr15xt0wow/I11DV58mQ+++wzrl+/jomJCVu2bOHhw4d4e3uXyPo+JVE122qYGJiQmJrIvWf3tO7T/LHSrLgsCEL5NXr0aNatW4eenh5r167lzTffJCkpKcfHKDmE4SWkinNOzM3lBGfNqu6dOkFAQHG3SijD8hX4BAQEMHToUAAMDAxISEjAwsKCb7/9lrlz5+q0gWWV1ppdGfJ8NLdFYrMgCCAPe/39998YGRmxZcsWOnfuTGhoaLbHKzmEJb3HR8PKSi5q2KgRhIXJwc/t28XdKqGMylfgY25urnzjqFSpEnfu3FHuCw8P103LyoGMeT6PYh6x9vJazj85D4jEZkEQXujXrx+7du3C2tqaEydO0Lx5c65du5blsZq/LUHPglh5cSWrfVez9vJaHsc8Lsom542trbychZcXBAfLwc/Dwlt6Izw+nL2392ZbRFYou/IV+LRs2ZKTJ08C0KNHDz777DO+++47hg8fTsuWLXXawLIs48yuQX8P4r1/3+NRzCP5ftHjIwhCOp07d+bMmTPUqFGD+/fv07p1a/79999MxzmaO+JsLtcu+3Dnh4zYPoL3/n2PN7e8WdRNzhsHB3kh01q14MEDOecnLKxQLjVq1yi6/9GdXTd3Fcr5hZIrX4HPwoULadGiBQDTp0+nS5cubNq0iapVq/Lrr7/qtIFlWfrAJ1WdyoUnFwDoWr0rczvPFTO6BEHIpE6dOpw9e5aOHTsSGxtL3759+eSTT0hMTNQ67sfuP9KrVi961upJl2pdALjw5AJp6rTiaHbuOTvDgQPg6gqBgXLicyGsoXj2kTx9/tzjczo/t1CyqaS8VMYqB2JiYrC2tiY6OhorK6tCvdbtyNvU/LEmJgYmXPrwEh5LPTAzNOP55OfoqcpciSVBEHQoJSWFiRMnsmjRIgDq1avHhg0b8PTM3FOcpk7DYrYFiamJ3PzoJjXtaxZxa/MhMBBeeUXu8WnXTs4BMjXVyaljkmKwnmMNQL86/dg6aKtOzisUr9x+fufr07VatWpERERk2v/s2TOqVauWn1OWS+427srMrp03dwJQ16GuCHoEQXgpQ0NDfvjhB3bv3o2TkxPXrl2jadOmzJ8/n5SUFK1j9fX0qesgrzJf4tbwyk7t2nKwY2UFx47BwIGQ4XnlV/qK+aXm9RB0Jl+fsPfu3SMtLXN3aVJSEo8fl+DkuRIm/cyuv/z/AkRejyAIedO9e3euXr3Kq6++SmJiIhMnTqRRo0YcP35c67iXLZNTIjVuLNf5MTGBnTth2DBQFzwZOf1rcDvyNompiTkcLZQ1eSpguH37duX/+/btw9raWrmdlpbGoUOHcHNz01njygNPR08uh1xWZnKJ2j2CIOSVs7Mzu3btYv369UyYMAE/Pz/atWvHO++8w7fffoubm1uOy+SUaO3awd9/Q9++8OefYGMDP/0EBSjumn6av1pSczPiJvWd6xe8rUKpkKfAp2/fvoC8Arumjo+GoaEhbm5uLFiwQGeNKw8yTlkXPT6CIOSHnp4ew4YNo3fv3kyZMoUVK1bw22+/sWHDBoYPH07zN5sDpai2T3o9esD69TBkCCxdCnZ2MGNGvk+XqXZaqJ8IfMqRPA11qdVq1Go1VapUITQ0VLmtVqtJSkoiMDCQnj17FlZby6SMgY6o3SMIQkHY2dnxyy+/cPbsWbp27UpqaiorVqxgVLdRsA38L/uX/JldWXnrLfj5Z/n/M2fCf0nd+aEUiS2tvWBCgeQrxycoKAgHBwetfc+ePdNFe8odTaExADNDM6ralKzFVAVBKJ2aNWvGvn37OHHiBJ06dZITni9DyvIUGjZuyMqVK4mKiiruZubNqFHw3Xfy/8eNg40b83yKmKQYpVbaAA95iSUR+JQv+Qp85s6dy6ZNm5TbAwYMwM7ODhcXF65cuaKzxpUHmpldIGZ0CYKge23atOHgwYOcOnUKuxZ2oA/Xr1znww8/xMnJia5du/LLL7+UnokpkyfD2LHy/999V17RPQ80ic2VLCvRpkoboJQO/wn5lq9P2eXLl+Pq6grAgQMHOHjwIHv37qV79+5MmDBBpw0s69JPMxX5PYIgFJZWrVrRY1IPGA+vjnoVLy8vUlNTOXDgAKNGjaJy5crUrFmTESNGsG7dOvz8/DJNi9eISYrhy0NfMnrXaCYfnExkQmSmY2KTY/nW51sCwuQFR88+OsucE3MKPMx2KcSXj7ukcbFtdXl6e9++JF84x8xjM7kS8vIv3pogx9PRUxnquhN1h9G7RrM9cHtODxXKiDwlN2sEBwcrgc/OnTsZOHAgXbt2xc3NTanoLORe44qN8Q3xpXGFxsXdFEEQyjAPRw8wB9sWtuxZuodbt27xzz//sHXrVs6fP8/t27e5ffs2q1evBsDIyIi6devi6emJu7s77u7uuLm5cSjsELMvzQZTQA+sTaz5ou0XWtdaf2U9Xx/9Gt8QX/4Z9A+jdo3CN8SXek716FGrR76fw9g9Yzn18BQrvGHvQ2h//zlp3buyakg0Rxof4dC7h3J8vGZYy8PRgwoWFahgUYGQ2BCWXVjGuivriP4iGgO9fH00CqVEvn66tra2PHz4EFdXV/bu3cvMmTMBkCQpy/o+Qs5mdJhBs0rNeLv+28XdFEEQyjBND4dmuKdmzZpMnDiRiRMnEh0dzcmTJzl27BgnTpzg6tWrPH/+nCtXrmSfwqAHmMKcX+ewr/o+bG1tsbW1xc7ODp8QH4iCk3dPssd6D9d8r4ExnL51mu41uqOnl/cBB0mSuPr0KgDGFlb0fTOG+/9Uxfrmffb9Dr2sfZEkCVUOU901z93T0ROVSsW/b/7Lnlt7mHtyLvEp8dyOvK3UVxPKpnwtWfHRRx+xc+dOatasia+vL/fu3cPCwoJNmzYxd+5cLl26VBhtLRJFuWSFIAhCUbobdZfqS6pjrG9M3JQ49PX0sz1WrVZz//59rl27RkBAAPfv3ycoKIh79+5x68Et0uLz/yXXwMAAFxcXXF1dqVKlCjVr1sTDw4O6detSq1YtjI2Ns3zcg+gHVF1UFUM9Q173eJ2N1zeyyGsCb/3vR5wiEjnjAu4X7+DsnP0KAq4/uPIo5hEnh5+ktWtrZX+zlc248OQCfw/4m9c9Xs/3cxOKT24/v/PV4/PDDz/g5ubGw4cPmTdvHhYWFoA8BDZ69Oj8tVgQBEEoVG42bpgamJKQmsCdqDvUsq+V7bF6enrK8Fbv3r2V/ZIkYT3HmucJz/mt22+88/s7GCQasLbLWmKiY4iMjCQyMpKfjv1EcmwyJICTvhOh4aGQAKRAamoq9+/f5/79+5mua2hoSKNGjWjdujWtW7emXbt2ODvLK81r8nNq2deioXNDNl7fyBkesv0DO/5a9ISWjyH8rcGw/wQYZP54i06MVmZ0pZ9RC3IP0IUnF/AP8+d1ROBTluUr8DE0NOTzzz/PtP/TTz8taHsEQRCEQqKn0qOuY10uBV/CL9Qvx8AnO49iHvE8+TkGhgYMaDGAkT4jiUuJo2nXptR2qA1A8PNgFi5cqDzG0taS0KhQAIwx5sawGzx5/ISHDx/y4MEDbty4QUBAAP7+/kRHR3Pu3DnOnTvHokWLUKlUNG/enF69ehFZJRIkeSKIZjLIucfnuGvyhJ6D4dB6cDhyFv73P1i1KlN15/QzumxMbLTuEzV9yo9cBz7bt2+ne/fuGBoaai1dkZX03w4EQRCEksPT0ZNLwZfwD/OnX91+eX68JjCoaVcTYwNj6jrW5cKTC/iF+SmBT8bg4U7UHeX/SSSRYpFC69atyUiSJO7du8fp06c5deoUJ06c4MqVK5w9e5azZ8/KBzlD7KBYKjepDMjDdwCnq8CgN2DbZhV6q1dDlSrw9dda50+f35ORpgdIBD5lX64Dn759+xISEoKTk5OydEVWVCqVSHAWBEEooQras6FMB/+vx0UzROQX6kf/uv21jlGhQuJFGqnmtl+YHzXta2Y6t0qlUobXBg8eDMDjx4/ZtWsX27dvZ/e+3UhPJXYv2c2h5YfQ99AnrXUaOMrn3lFHYuHb1fl8/W2YPh2qVYN33nnR9gwVm7Vel/+eT2B4IClpKRjqG+br9RFKvlyn1avVapycnJT/Z7eJoEcQBKHk0nzA5zvwyRA8ZBVIaXpW2lZpq/VYze28rBDv4uLChx9+yI4dOzD7wgy6Q626tUhKSiLNNw1+BjZDI1UjAGZ7RiJp6smNGAFHj2Zqe8b8HoAq1lUwNzQnRZ2i1UMllD15nk+oVqtZvXo1PXv2xMvLi3r16tGnTx/Wr19PPiaICYIgCEVIE6jcCL9Bqjo1z4/PFPhkEUhp/q9ZEgLA3NCc7jW6Zzo2tx5EPyDOIA7DVoZcu3qNM2fOUKVFFflOf7j09SX4GyKfRvJ06qcwYIBc4LBfP7hxQ75uht6q9PRUei+Gu0Ql5zItT4GPJEn07t2b999/n8ePH1OvXj08PT25d+8ew4YNo1+/vI8XC4IgCEWnqk1VzAzNSE5LVvJjckuSpBd5Mv8FD5pgITA8kFR1KpIkKYGNt5s3TuZOynFeTl5A/gILzXVrO9TGyMCIFi1aMGbBGBgFeMrDZFwHfoRps6aTvHIltGoFz57Ba68R8+A2j58/1mpzRiLPp3zIU+Czdu1ajh07xqFDh/D19WXDhg1s3LiRK1eucPDgQQ4fPsz69esLq62CIAhCAemp9JRlcnIKQHzu+dBrQy+CooKUfQ9jHhKbHIuBngE17GoA2kNEtyNvExIbwrPEZ+ip9KhlX0urZ0gTWNwIv5HrpStC40Lp8WcPRu+WS6Wkz8/xdPQEZ2AAHD55GNuatpACK+euxK6uC/snfopUrRoEBRHUvj4mKeBi6ZJpRpfW+RCBT1mXp8Bnw4YNTJkyhQ4dOmS6r2PHjnzxxRf88ccfOmucIAiCoHu5yfNZcHoBO2/uZO3ltcq+9HV0jPSNgMxDRJpz1rCrgYmBiZLX08a1De627pgamJKUlpTr3qZN1zex+9Zu7j27p5xHo0mlJhjrG1PHoQ7tW7Xni9VfQF/AHOKC4+j+xluMadeUUGNoEJTAb1uhbeXMs8kyvS5iqKtMy1Pgc/XqVV599dVs7+/evbtYnV0QBKGEy03Phua+rHJ3Ms6KSh9IpV8EFGBqu6lc+OAC7zV8T6kj9LJrZ9WOt7ze4ujQo4xqNkq5r4JFBa6OuorPMB8Axrcez5EfjtDnpz5QD9Rpapat3UwNKwjQhzcCYP3ZStleS9PmmxE3SUnLeoFWofTLU+ATGRmpVNDMirOzM1FRUQVulCAIglB4ND002c2uik+JV4a40h+TXeDj4fAiNybjzCkjfSOaVGqiLI+R1wRizfl61OyBt5t3pgVEa9nXUvKIDPQMaO/Wnt4NesPr4DnSE2MzY56HQRMDA/YCRgsXw/LlWV7L1dpVa9hOKJvyFPikpaVhkEUZcA19fX1SU/M+S0AQBEEoOi+b2XUj/IZSf+dW5C2S05IBMiU2K+dzerH4aU5FAtPvz02PjyRJOc7Eyo7mGpE1Imk7uy24QkJSKj1UKhYA0ujRsH9/psdpDduJPJ8yK09LVkiSxLBhw7JdQC4pKUknjRIEQRAKj2ZmV3xKPHci7ygVlzXS98akqlO5GXETT0fPbIMaze3A8EDMDM3kfdkEKhlXiM/J07inRCVGoafSo7Z97Zcer6EZTguODSbVLBWGwms3X2P35t18DlxVq1k+YAAmZ89CHe2V2D2dPDn/5Dx+oX684fFGrq8plB556vEZOnQoTk5OWFtbZ7k5OTnx7rvvFlZbBUEQBB14Wc9Gxn3+Yf48iH5AbHIshnqGyowujSrWVbAwsiBFnUJ0UnSOgYomIMrNzC5NAFbNthqmhqa5e3KAlbEVrlauAITFh4EBrP51NUuWLEFfX5/1QPeYGJ736AGRkdrt0wRm4bkvsiiULnnq8VmzZk1htUMQBEEoQh6OHspq5JqlJjQ0vTFG+kYkpyXjF+qHhZEFIOfUZFzOQaVSUdehLuefnAfkGV3GBlmPDORlhficlph4GU8nTx7GPATAwcwBZwtnxo4dS926denfrx9HY2PpePcue/r0weHwYTA01LqWmNlVduW5crMgCIJQ+uWUa6PZl77S8stybdLvzylQ0ZrZ9ZLgIuMMsbxI/5j0BQs7d+7MkaNHcbC15QLQ7sQJHg0fDv+tPKA5VszsKrtE4CMIglAOZdezkX5Gl2bJCf8wf2Xo52VJyzkdk/H+l+X5KNfMQ2JzbtrTpEkTjp86RWUHBwKAtr//zv0ZMwDtYbtbkbfyfF2h5BOBjyAIQjmkrEYeEag1sysgLAAJCQczB7zdvAF5ZpdvsK/8uNwEPi8JVHIzsyv9jK7slpjISfrHZNXmOnXqcPLiRWo6OHAf6PT11zzesAGVSiXW7CrjROAjCIJQDmmWmkhOS+ZO5IvVyNPP3HKxdMHK2IpUdSpXnsrFaV+2zlVOx2S8f8P1DRjPNOancz9lOiYkNkSZ0VXHoU6m+18mN+2pUqUKR3x9qWZhwR2g89tvE3ryZJ5mngmljwh8BEEQyqHsqiinTyhWqVT0q/Ni8WkPRw9q2tfM8nxVrKvQuGJjatvXfmmg0qZKGxzNHAFITktm/ZXMazxq2lHdtjomBiZ5eGYyS2NLOlfrjIulC00rNc32OJfKlTl04QKVjYy4oVbTpWNHaic7abVBKFtE4CMIglBOZZXnowQ+/w1XremzhtDPQwn5LIQrI69kqpysoVKpOPf+Oa6Pvq6s45UdO1M7Ho57yIn3TgByz4r0X3Kx0o58FC7MaP/b+wn6JAhLY8scj3OrXZvDPj5U0NfnanIyfw3/Ef1EEfiUVSLwEQRBKKeyyrXJOJNKpVLhaO6Is4VztkGPhr6e/kuP0TA2MKa5S3MM9QyJS4njQfQDrftfVgE6N1QqVaap99mp2bIlBzdvxg64GBtP3eUQGBaoVK0Wyg4R+AiCIJRT6ZeaAIhLjlNWQc9PQnFeGeobKnV8MvauZFzzqyh49u/Pv999hzFwPQpqrEvjVoSY2VXWiMBHEAShnNIEFZqZXZo1uhzNHHE0dyySNigru6cbbpMkqUDFCwui7ZQp/PbGG6iAwCBYMn5CkV5fKHwi8BEEQSin0s/suh15O1N+T1HIargtODaYZ4nP5KUvHHK/RpeuDNi0iXGuNgCs+GMPW379tcjbIBQeEfgIgiCUU1prdoX6FahScn7llGdUw65GvmZ0FZieHlVXjuft/y499MMPuXrxYtG3QygUZSrwcXNzQ6VSaW1ffPFFcTdLEAShxFKGmsL8imV4SXP9gLAA1JIa0E1ic0HVqt6Ms8Ohgx7EqdX06dCB8PDwYmuPoDtlKvAB+PbbbwkODla2qVOnFneTBEEQSqz0xfo0AUdRJhRXt62eaWZXcSQ2Z+Tp6MktJzDpp6IacO/58/+3d+fxNZ2JH8c/N9tNZCMikhCS1lahiKVCq5ZBFd3UNtOWLooKVdrpYmprVdspOp1WTU1/tKOlDFXVlhZBFUMJJdTSqkTFXkkkskjO74/bXIlsN+rem7jf9+t1X3PvOc85nvO8zky+c86zMOC228jN1fpdVd11F3z8/f0JDQ21fvz8/JxdJRGRSqsgXGz7dRtHzlvW6HJkHx9Pd09rP56VB1ey4/gOdqRYXis584lP3YC6+Hv581Vzg1l/7oUfEP/jj4z785+v+pzZl7I5lXHK+vvkhZMaLu8E113wee2116hZsyYtW7Zk2rRp5OSUfVNlZ2eTlpZW5CMi4ioKwkVB6AnxDSG4WrBT6jD6q9G0mduGnSk7LdsdGMCuVHjNrnsafMXoCBMAb//3vyycPfuqztn7495EzIogOTWZQ2cPUWdmHfov6X/N6iy2ua6Cz5NPPsmiRYuIj48nLi6ON998kyeeeKLMY6ZPn05gYKD1ExER4aDaiog4X73Aejx484PUDahLvcB6PB37tMPrMCxmGI1qNqJuQF3r594m9zr1iQ/AmFvGEFU9Ck9PL/75gMEzNQIs9R09mh9/+KFC58rNy2Xj0Y3k5OWwOXkz3yZ9S56Rx7oj64rNWi32ZTIqeYtPnjyZKVOmlFlm+/bttGlTfC2WpUuXcv/993PmzBlq1qxZ4rHZ2dlkZ2dbf6elpREREUFqaioBAQF/rPIiIlLl/WXZX/h4z8e8c+NY/jv0n8Tn5REdFMT/kpLw9fW16Rz7T++n6WzLE6QXO71IRk4GM7fOBODo2KPUC6xnt/q7irS0NAIDA8v9+23b3OJOFBcXx6BBg8osExkZWeL29u3bA3D48OFSg4/ZbMZsNv+hOoqIyPWr4MnT5mqn+Xj+fFo9+CCJ587xxB13MH/jRkwmU7nnuHIh2IycjMu/TyUq+DhQpQ8+wcHBBAdf3fvmhIQEAMLCwq5llURExIUUHvkWOnwBC1evptuCBXy4aROdpk7l0UmTyj1HwYg5sASdjNxCwed0Ir0a9rr2FZcSXTd9fLZs2cKsWbPYtWsXR44cYfHixQwfPpy77rqLevWUpEVE5OpY5xo6s5+8/Dw6z5/Pyw0aABA3ZQq7N20q9xyFn/gcOneIY2nHrL8LhyKxv+sm+JjNZj755BM6d+5M06ZNmThxIsOGDWPhwoXOrpqIiFRhUdWj8PbwJutSlmX0m7s7z373HXd6e5NlGNx/xx2kpaaWeY7Ca5EVTNRo3XfFAq1iX9dN8ImJiWHr1q2cP3+eixcv8uOPPzJ58mSqVavm7KqJiEgV5u7mTpPgJsDlAOMWEsKHK1YQARzOyOCJLl1KPT43L5eDZw8ClvmBChR833d6n0Z2OdB1E3xERETspXA/nwI1u3dn0dNP4w58lJDAggkTSjz28LnD5Obn4uflR88be1q339XoLjzdPLmQc8E6a7XYn4KPiIhIOUpaTBWgw+uvM6llSwBGTp/OT999V+zYwktwNAtpZt3eMrQljWo2AtTPx5Eq/aguERERZyvo4Lw5eTNzd8ylV8NelldVJhMvbNzIN2FhfJuRwcCe3Xn0q1dx87k8Tcrqn1ZbzlErusikjNEh0USHRFsXiNXILsdQ8BERESlH85DmgGVpj8dXPk63qG6seWgNAO7+/iz48kta3H47OzIuUmPQk6x5vPg5moU0sz7xMWFZEqO0J0liPwo+IiIi5YiqEcX0btPZcHQDqw6vYvvx7RiGYZ28sF6nTrw4YjDj5yxk7XEY/kNjTg24vLp8kE8QQ1oMoWa1mszsMROzh5nq3tWt64EVHvUl9lXpl6xwNFunvBYREdeTk5eD7yu+XMq/RNLYJCICL6/v+O72d/m/vk/w/UmoYzKx+3//o2bbtmWer2ApC19PX9KeT8PNpK63V8vWv99qYRERERt5uXtZOyRf+Xpq3+l97BwKUV4e/GoYPNa9O8bFi2Wer0FQAzzdPMnIzSA5Ndle1ZZCFHxEREQqwNov54rXU4mnE8n3geEzxuAJLE9N5b1eZXdY9nT3pHFwY+vxYn8KPiIiIhVQ0C/nyiHoBcGl672DmP7YYwA8tWED+2bMsOl86ufjGAo+IiIiFVDSSKwzmWc4lXEKgJtq3cRT//oXPaKiuAgM+utfydq7t0LnE/tR8BEREamAgjl9Ci81UfD0J7J6JH5efri5ufHBxo3U8vRkT34+f+3SBbKySj5fCbNCi/0o+IiIiFRAg6AGeLh5kJ6TTnKapUNywWuqgtdWAKF16zL/3/8G4J9nzrDyvvtKPF/hIHXlAqZy7Sn4iIiIVECRkV2/B56C11SFZ2YGuPOhh3jynnsAePirr0iZM6fY+W6scaN1ZJfW7LI/BR8REZEKKgg4076dxrAVw/j84OdFthf22qJFtAgJ4QzwUFwc+QcOFNlfZGSXOjjbnYKPiIhIBbUOaw3Ad8nf8e+Ef1uf1MSExRQrazabWbhmDT5ubqzJy+ONEvr7qJ+P42jJChERkQoa1W4Uvl6+pGWnWbc1DGpI89rNSyx/U/Pm/GP6dB5/9lkmpKTQ5YEHaPvf/1r3a2SX4yj4iIiIVJCflx9x7eIqdMxjzzzD6s8/Z+mmTQxeupSERYvwHzQIKDSXj4KP3elVl4iIiAOYTCbmrlhBhL8/PwFxQ4bAsWOARnY5koKPiIiIg9SoUYOPli/HDfgwJ4ePu3eHvDwaBDXAy92LzNxMjeyyMwUfERERB7qta1f+NmoUAMN//JED48bh4eZB45oa2eUICj4iIiIO9uKbb9KpcWMuAP3feovMdevUz8dBFHxEREQczMPDg0Xr1hHi7c0eIO6uu4jxvgFQ8LE3BR8REREnCAsPZ+HixbgB8zIycH9+MRh61WVvCj4iIiJO0rVvX6YMHw7A3/b+xN3rYf+Z/RrZZUcKPiIiIk70wuzZ3NGoEVnArg0Q/lMmLee0ZPzq8QD8/NvP9PqoF2t/XlvkuGe+foYnv3rSukK82EbBR0RExInc3Nz46LvvuMHHh6NAjf/AgaQ9zNw6k+Ppx/lw94esOryKmVtnWo85eeEkb2x5g7e2vWVdIV5so+AjIiLiZEHBwXz2xRf4AttzodOHnoBlQsOCzs6F+/4UXtNLfYIqRsFHRESkEmjWpQv/mTABgDXHc4lebQk1BSHnaOpRLuRcAIqO/NLCphWj4CMiIlJJ3Pvyy0xq2xaAg1tg/WefcfDsQev+/af3A0Wf8mj4e8Uo+IiIiFQiE9et425fb3KBb16P59LxS9Z91tdepxV8rpaCj4iISCXi5ufHy5+8R0cgIx+qzQdSLfsSTyViGEaxV10a2WU7BR8REZFKpkHP/rS+HW4CMrPA/KEJLlqe7pzKOMW5i+cwYcLDzYMLORe0sGkFKPiIiIhUMt4e3nx9X0Mm14EwIPusAR/BnuQ91qc9NwbdeHlhU73uspmCj4iISCV0U+1mPNMfFntCDYBjcGz2MTYc2ABA01pNrQubamSX7RR8REREKqHoWtEkVYd3+8Iafg8/yTAjbgZkWfZH14oGYM+pPVzKv2Tzx5WXxPBwdgVERESkuIKnOSvb+LPAqzdrFi2iiwnSfsqAjyCydyRBQUEAfLj7Qz7c/aHN5/b19GXF4BV0jepql7pXZnriIyIiUgl1iepCTZ+a9Gt6P6bZs2kYUp11BlRzB5Lh74/+nXr59ahVrVaFz52Rm8HSfUuvfaWrAD3xERERqYRC/UI58fQJPNwsf6r9Fy0jpls3NucZ9K4ZxOGDh+nTtQ/LPl1G05imNp93ceJiRn4xkn1nXLNfkJ74iIiIVFIFoQeALl0wjR9PC+B/+Qatmjfn9OnTdP9Td77+7GuCfIJs+rQJbwO47hpfCj4iIiJVxcsvw803U+e339gYEUGfPn3Iyspi8ODBPPLII6Snp5d7ipuCbwLgdOZpTmectneNKx0FHxERkarCbIb//Ac8PfH78kuW9+vHCy+8gMlkYt68ebRq1Ypt27aVeQpfL1+iqkcBrjn/j4KPiIhIVXLzzTBlCgDuTz7JtBEjiI+PJyIigp9++okOHTowbtw4fvvtt1JPER1iGQbviq+7FHxERESqmmeegVtugbQ0eOQRbu/Uid27dzNw4EDy8vKYNWsWDRs2ZPbs2Vy6dKnY4QXz/7jixIdVJvhMmzaNDh06UK1aNapXr15imaSkJPr27Yuvry/BwcGMGTOGnJwcx1ZURETE3jw84IMPwMcH1qyBOXOoUaMGixYtYtWqVTRt2pSzZ88yatQooqOjmTNnDpmZmdbDC+YI0quuSiwnJ4f+/fszcuTIEvfn5eXRu3dvMjIy2LRpE4sWLWLp0qWMHz/ewTUVERFxgMaN4dVXLd+ffhoOHwagZ8+e7N69m9mzZ1OzZk0OHjzIyJEjiYiI4IUXXuCXX36xPvFxxeBjMqrYWvbz589n7NixnD9/vsj2r776ij59+pCcnEx4eDgAixYtYujQoZw6dYqAgACbzp+WlkZgYCCpqak2HyMiIuIU+fnQrRusXw8dO8KGDeDubt2dnp7OvHnzePPNNzly5Ih1e2yHWLZU3wLRsHr4amLCYgiuFlzs9Dl5OZy8cJKIwAhHXM0fYuvf7yrzxKc8W7ZsoVmzZtbQA5bUm52dzY4dO0o9Ljs7m7S0tCIfERGRKsHNDebNA39/+O47mDWryG5/f3/GjBnDoUOHWLZsGd26dcNkMrFl8xb4EnjD8rcy8qFIjqYcLXb6IcuHUO/NeiSkJDjoguzvugk+J06coHbt2kW21ahRAy8vL06cOFHqcdOnTycwMND6iYio/KlWRETEKjLycuCZMAESi7++cnd3595772XNmjUcO3aMGTNmENEkAgzgJ8hYmsGN9W7kjjvu4P333yc1NRWA+CPxAGw8utFBF2N/Tg0+kydPxmQylfn5/vvvbT6fyWQqts0wjBK3F3j++edJTU21fpKTk6/qWkRERJzmkUfgzjshJwceeghyc0stGh4ezrhx40jan8TBgwdpcH8DqA15l/JYvXo1jz32GOHh4Tz48IOc/OUkcH31BXLqWl1xcXEMGjSozDKRkZE2nSs0NJT//e9/Rbb99ttv5ObmFnsSVJjZbMZsNtv0b4iIiFRKJhPMnQvNmsHOnfDKKzBpUrmHNWzYkD6P9uHNZm8ypM4QGp5syMcff8y+fftYMH+BpVAz2FltJ/S18zU4iFOf+AQHB9OkSZMyP97e3jadKzY2lr1795KSkmLd9vXXX2M2m2ndurW9LkFERKRyCA+H2bMt319+Gcro31pYwWSGKd4pTJgwgb179/Ltt9/SonMLS4G9sGPCDmbMmEF+fr49au5QVaaPT1JSErt27SIpKYm8vDx27drFrl27uHDhAgA9evSgadOmPPjggyQkJLB27Vqefvpphg0bptFZIiLiGgYOhP794dIlGDIEsrPLPcQ6p8/vszibTCZuvfVWbnvmNhgO1ANy4emnn+buu+8uc0boqqDKBJ+JEyfSqlUrJk2axIULF2jVqhWtWrWy9gFyd3fniy++wNvbm44dOzJgwADuuece3njjDSfXXERExEFMJstTn1q1LJ2cp00r95CC4PNr+q+czzpv3Z54OhHCgIeBPuDp5cnKlSuJjY3l+PHj9qm/A1S5eXzsTfP4iIhIlbdkCQwYYJnheft2aNmyzOJ1Z9bl1/Rf2fzIZmIjYgEIfSOUkxknqRdYj6TUJMZHjmfxi4tJTk6mYcOGxMfHU6dOHQdcjG1cbh4fERER+V3//tCvn+WV18MPlznKC4ovYXE28ywnMywjuvrd1A+A1BqpbNy4kfr163Po0CHuvPNOMjIy7HgR9qHgIyIicj165x2oWRN27bq8tEUprEtY/N7PpyAARVaPpF2ddtZtkZGRrF+/ntq1a/PDDz8wbNgwqtqLIwUfERGR61Ht2vDWW5bvL70Ee/aUWrRgZNeaI2t4e9vbzN05F7A8CSq8krthGERGRrJkyRI8PDxYuHAh7777rn2v4xpT8BEREbleDR4Md91ledX18MOWV18laB7SHIC9p/Yy+qvRLPhhgXV7o5qNcDe5k5qdyvF0S6fm2267jb///e8APPvss1Vq8l8FHxERkeuVyQRz5kD16pZ5fUoZ6dyuTjsmdprIgOgB1s+jrR4lrl0cZg8zDYIaAEVncB4zZgwdOnTgwoULxMXFVZlXXhrVdQWN6hIRkevOBx/A0KHg5WXp83PTTRU6vN/ifizbv4yZPWbyVOxT1u2JiYm0atWK3NxcVq5cSe/eva9tvStAo7pERETE4qGHoFcvy1pejzwCeXkVOtza+fmKNbuio6N58sknAct8e1XhWYqCj4iIyPXOZIL33oOAANi6Fd58s0KHF+7gfKVnn30WPz8/du7cyYoVK65Fbe1KwUdERMQV1K0LM2ZYvv/tb3DwoM2HFoz6SjydWOypTnBwMGPGjAFg0qRJlX49LwUfERERV/Hoo9C9O2RlWb7bGFIaBjXE3eROWnYav6b/Wmz/+PHj8ff3Z/fu3axdu/Za1/qaUvARERFxFSYTzJ0Lfn6waRPYOAeP2cNMw5oNgcuTHBYWFBTEkCFDACr9vD4KPiIiIq6kfn147TXL9+eeAxvn4Cmtg3OBESNGALBixQqOHTv2x+tpJwo+IiIirmbECOjQAS5cgCeeABtGY5XVwRksI7w6depEXl4ec+fOvabVvZY8nF0BERERcTA3N8srr1atYOVKWLwYBg4s85CCDs5fHvqSx1Y8xsg2I2kd3pqtx7Yyd8dc8ow86v+pPmyEuf+eS2aHTE5fPF3iuV7906uE+oVe88uyhSYwvIImMBQREZcxdSpMmgQhIbB/PwQFlVr08LnDNPxnQ+vvrlFdWfvQWmLfj2Xrsa2WjZfA/y1/0tPS4WGgfsnnOhB3gEY1G13DC7H977ee+IiIiLiq556zPO1JTISnn4b/+79SizYIasDngz9nwy8beGPLG+w5uYd8I589Jy2Ln9bwrsFvWb8R0yWGDZ9tgETofHtn7mxwZ7FzBVcLttsllUfBR0RExFV5eVleeXXsCPPmwZ//DH/6U6nF+zTqQ9eorszYMoPTmafZmbKTjNwMPN086d2oNwt+WEDd9nXhM2AfDGgygJG3jHTc9dhAnZtFRERcWWwsjBpl+T58OGRmllm8mmc1IqtHArAkcQkAjYMb06J2CwCy6mdh8jbBBbh0tOTV4J1JwUdERMTVvfKKZWbnn3+GyZPLLV7Q0XnxvsUANK3VlKa1mgLw/cnvMZpYug/vXrfbPvX9AxR8REREXJ2//+XJDGfMgJ07yyxeMLT9l/O/WH8XbDuaehR+X/x9zao1lW7hUgUfERERgT59YNAgyzIWjz4KubmlFi0IOYV/1wush5+Xn2VDFJg8TBw9epRDhw7Zs9YVpuAjIiIiFv/4h2VI+65dMGtWqcUKXmsViA6JxmQyXd7uBXWi6wCwevVqe9X2qij4iIiIiEVICMycafk+aRIcPlxisZtq3YQJEwCebp40CGoAFH0S1Pa2toCCj4iIiFRmDz1kGdKelWUZ5VVCH51qntWIqhEFWEZ0ebhZZscp/CSo7519AYiPjyc7O9sBFbeNgo+IiIhcZjLBv/4FPj6wbh0sWFBisYKQU/gpT+HvfTv1pXbt2mRmZrJ582b71rkCFHxERESkqBtugIkTLd/HjYOzZ4sVub3+7QB0qt/Juq1NeBt8PHyIrhVNsG8w3bt3B+Cbb76xf51tpOAjIiIixY0fD9HRcOYMPPtssd1PtX+KxCcSGdFmhHVbLd9aHIg7wLcPfwtA586dAfjuu+8cUmVbKPiIiIhIcZ6elldeAO+/D99+W2S3u5s7TWs1xc1UNEpEBEZQw6cGAB07dgRg27Zt5OTk2L/ONlDwERERkZJ17AjDhlm+jxgBFQwvjRo1IigoiKysLHbt2nXt63cVFHxERESkdK++CrVqwb59llmdK8DNzY0OHToAled1l4KPiIiIlC4o6PLcPlOnWtbzqoCC110KPiIiIlI1/OUv0K2bZW6fJ54ocW6f0hR+4lMZ1u1S8BEREZGymUwwezZ4ecHq1bB4sc2Htm3bFg8PD06cOMEvv/xivzraSMFHREREyteoEbzwguX72LFw/rxNh/n4+BATEwPAli1b7FO3ClDwEREREds895wlAJ04ARMm2HxYmzZtAEhISLBXzWym4CMiIiK2MZthzhzL93ffhW3bbDqsVatWgIKPiIiIVDVdulgWMjUMePxxuHSp3EMKBx9nd3BW8BEREZGKeeMNyzD33bvhH/8ot3izZs3w8PDg3LlzJCcnO6CCpVPwERERkYqpVQtef93yffJk+PXXMoubzWaaNrWs5u7s110KPiIiIlJxDz8MsbFw4YJlQdNyVJZ+Pgo+IiIiUnFubvDOO5b//OQTWLu2zOIKPiIiIlK1tWplmckZIC6uzEVMFXxERESk6nvpJQgJgR9/hFmzSi3WsmVLAJKTkzl79qyDKldclQk+06ZNo0OHDlSrVo3q1auXWMZkMhX7zCmYb0BERESuverV4e9/t3yfOhWSkkosFhAQwI033oiPjw+HDx92XP2uUGWCT05ODv3792fkyJFllps3bx4pKSnWz5AhQxxUQxERERf14INw662QmQnjxpVabMOGDaSlpXHLLbc4sHJFeTjtX66gKVOmADB//vwyy1WvXp3Q0FAH1EhERESAy4uYtmoFS5daFjLt2bNYsTp16jihckVVmSc+toqLiyM4OJi2bdsyZ84c8vPzyyyfnZ1NWlpakY+IiIhUUPPmMGaM5XtcHGRnO7c+pbiugs9LL73EkiVLWLNmDYMGDWL8+PG88sorZR4zffp0AgMDrZ+IiAgH1VZEROQ6M3kyhIXB4cOX+/1UMibDiYtmTJ482foKqzTbt2+3ruoKllddY8eO5fz58+Wef8aMGUydOpXU1NRSy2RnZ5NdKJWmpaURERFBamoqAQEB5V+EiIiIXLZwIfz5z+DtDfv3Q2SkQ/7ZtLQ0AgMDy/377dQ+PnFxcQwaNKjMMpF/oMHat29PWloaJ0+epHbt2iWWMZvNmM3mq/43REREpJBBg2DuXIiPhyefhM8+c3aNinBq8AkODiY4ONhu509ISMDb27vU4e8iIiJyjZlM8Pbb0KIFrFgBK1dCnz7OrpVVlRnVlZSUxLlz50hKSiIvL49du3YB0KBBA/z8/Pj88885ceIEsbGx+Pj4EB8fz4QJE3j88cf1REdERMSRmjaFp56y9PMZMwa6dQMfH2fXCnByH5+KGDp0KB988EGx7fHx8XTu3JlVq1bx/PPPc/jwYfLz87nhhht47LHHGDVqFB4etuc7W98RioiISBkuXIAmTSwrt0+aZOn4bEe2/v2uMsHHURR8RERErpElS2DAAEtH5337ICrKbv+UrX+/r6vh7CIiIlKJ3H8/dOkCWVllzujsSAo+IiIiYh8mE/zzn+DuDsuXw9dfO7tGCj4iIiJiR9HRMHq05fuYMZCT49TqKPiIiIiIfU2eDCEhcOAA/OMfTq2Kgo+IiIjYV2AgvPaa5fvUqXD8uNOqouAjIiIi9vfQQ9C+PdSu7dTgU2UmMBQREZEqzM3NMrw9ONgyvN1JFHxERETEMerWdXYN9KpLREREXIeCj4iIiLgMBR8RERFxGQo+IiIi4jIUfERERMRlKPiIiIiIy1DwEREREZeh4CMiIiIuQ8FHREREXIaCj4iIiLgMBR8RERFxGQo+IiIi4jIUfERERMRlaHX2KxiGAUBaWpqTayIiIiK2Kvi7XfB3vDQKPldIT08HICIiwsk1ERERkYpKT08nMDCw1P0mo7xo5GLy8/M5fvw4/v7+mEyma3betLQ0IiIiSE5OJiAg4JqdV4pSOzuO2tox1M6OoXZ2DHu2s2EYpKenEx4ejptb6T159MTnCm5ubtStW9du5w8ICNB/qRxA7ew4amvHUDs7htrZMezVzmU96Smgzs0iIiLiMhR8RERExGUo+DiI2Wxm0qRJmM1mZ1fluqZ2dhy1tWOonR1D7ewYlaGd1blZREREXIae+IiIiIjLUPARERERl6HgIyIiIi5DwUdERERchoKPg8yePZuoqCi8vb1p3bo13377rbOrVKVNnjwZk8lU5BMaGmrdbxgGkydPJjw8HB8fHzp37kxiYqITa1w1bNy4kb59+xIeHo7JZGL58uVF9tvSrtnZ2YwePZrg4GB8fX256667OHbsmAOvovIrr52HDh1a7P5u3759kTJq5/JNnz6dtm3b4u/vT0hICPfccw8HDhwoUkb39B9nSztXpntawccBPvnkE8aOHcuECRNISEjgtttuo1evXiQlJTm7alVadHQ0KSkp1s+ePXus+15//XVmzpzJ22+/zfbt2wkNDaV79+7WtdikZBkZGbRo0YK33367xP22tOvYsWP59NNPWbRoEZs2beLChQv06dOHvLw8R11GpVdeOwPccccdRe7vL7/8ssh+tXP5NmzYwKhRo9i6dSvffPMNly5dokePHmRkZFjL6J7+42xpZ6hE97QhdteuXTtjxIgRRbY1adLEeO6555xUo6pv0qRJRosWLUrcl5+fb4SGhhqvvvqqdVtWVpYRGBhozJkzx0E1rPoA49NPP7X+tqVdz58/b3h6ehqLFi2ylvn1118NNzc3Y9WqVQ6re1VyZTsbhmEMGTLEuPvuu0s9Ru18dU6dOmUAxoYNGwzD0D1tL1e2s2FUrntaT3zsLCcnhx07dtCjR48i23v06MHmzZudVKvrw6FDhwgPDycqKopBgwbx888/A3DkyBFOnDhRpM3NZjO333672vwPsKVdd+zYQW5ubpEy4eHhNGvWTG1fQevXryckJIRGjRoxbNgwTp06Zd2ndr46qampAAQFBQG6p+3lynYuUFnuaQUfOztz5gx5eXnUrl27yPbatWtz4sQJJ9Wq6rvlllv48MMPWb16NXPnzuXEiRN06NCBs2fPWttVbX5t2dKuJ06cwMvLixo1apRaRsrXq1cvPvroI9atW8eMGTPYvn07Xbt2JTs7G1A7Xw3DMBg3bhy33norzZo1A3RP20NJ7QyV657W6uwOYjKZivw2DKPYNrFdr169rN+bN29ObGwsN954Ix988IG1w5za3D6upl3V9hUzcOBA6/dmzZrRpk0b6tevzxdffMF9991X6nFq59LFxcXxww8/sGnTpmL7dE9fO6W1c2W6p/XEx86Cg4Nxd3cvllhPnTpV7P9lyNXz9fWlefPmHDp0yDq6S21+bdnSrqGhoeTk5PDbb7+VWkYqLiwsjPr163Po0CFA7VxRo0ePZsWKFcTHx1O3bl3rdt3T11Zp7VwSZ97TCj525uXlRevWrfnmm2+KbP/mm2/o0KGDk2p1/cnOzmb//v2EhYURFRVFaGhokTbPyclhw4YNavM/wJZ2bd26NZ6enkXKpKSksHfvXrX9H3D27FmSk5MJCwsD1M62MgyDuLg4li1bxrp164iKiiqyX/f0tVFeO5fEqff0Ne0qLSVatGiR4enpabz//vvGvn37jLFjxxq+vr7GL7/84uyqVVnjx4831q9fb/z888/G1q1bjT59+hj+/v7WNn311VeNwMBAY9myZcaePXuMwYMHG2FhYUZaWpqTa165paenGwkJCUZCQoIBGDNnzjQSEhKMo0ePGoZhW7uOGDHCqFu3rrFmzRpj586dRteuXY0WLVoYly5dctZlVTpltXN6eroxfvx4Y/PmzcaRI0eM+Ph4IzY21qhTp47auYJGjhxpBAYGGuvXrzdSUlKsn8zMTGsZ3dN/XHntXNnuaQUfB3nnnXeM+vXrG15eXkZMTEyRYX5ScQMHDjTCwsIMT09PIzw83LjvvvuMxMRE6/78/Hxj0qRJRmhoqGE2m41OnToZe/bscWKNq4b4+HgDKPYZMmSIYRi2tevFixeNuLg4IygoyPDx8TH69OljJCUlOeFqKq+y2jkzM9Po0aOHUatWLcPT09OoV6+eMWTIkGJtqHYuX0ltDBjz5s2zltE9/ceV186V7Z42/V5pERERkeue+viIiIiIy1DwEREREZeh4CMiIiIuQ8FHREREXIaCj4iIiLgMBR8RERFxGQo+IiIi4jIUfERERMRlKPiISJVw6tQphg8fTr169TCbzYSGhtKzZ0+2bNkCWFbYXr58uXMrKSKVnoezKyAiYot+/fqRm5vLBx98wA033MDJkydZu3Yt586dc3bVRKQK0RMfEan0zp8/z6ZNm3jttdfo0qUL9evXp127djz//PP07t2byMhIAO69915MJpP1N8Dnn39O69at8fb25oYbbmDKlClcunTJut9kMvHuu+/Sq1cvfHx8iIqKYsmSJdb9OTk5xMXFERYWhre3N5GRkUyfPt1Rly4i15iCj4hUen5+fvj5+bF8+XKys7OL7d++fTsA8+bNIyUlxfp79erVPPDAA4wZM4Z9+/bxr3/9i/nz5zNt2rQix7/44ov069eP3bt388ADDzB48GD2798PwFtvvcWKFStYvHgxBw4cYMGCBUWClYhULVqkVESqhKVLlzJs2DAuXrxITEwMt99+O4MGDeLmm28GLE9uPv30U+655x7rMZ06daJXr148//zz1m0LFizgr3/9K8ePH7ceN2LECN59911rmfbt2xMTE8Ps2bMZM2YMiYmJrFmzBpPJ5JiLFRG70RMfEakS+vXrx/Hjx1mxYgU9e/Zk/fr1xMTEMH/+/FKP2bFjB1OnTrU+MfLz82PYsGGkpKSQmZlpLRcbG1vkuNjYWOsTn6FDh7Jr1y4aN27MmDFj+Prrr+1yfSLiGAo+IlJleHt70717dyZOnMjmzZsZOnQokyZNKrV8fn4+U6ZMYdeuXdbPnj17OHToEN7e3mX+WwVPd2JiYjhy5AgvvfQSFy9eZMCAAdx///3X9LpExHEUfESkymratCkZGRkAeHp6kpeXV2R/TEwMBw4coEGDBsU+bm6X/+dv69atRY7bunUrTZo0sf4OCAhg4MCBzJ07l08++YSlS5dqNJlIFaXh7CJS6Z09e5b+/fvzyCOPcPPNN+Pv78/333/P66+/zt133w1AZGQka9eupWPHjpjNZmrUqMHEiRPp06cPERER9O/fHzc3N3744Qf27NnDyy+/bD3/kiVLaNOmDbfeeisfffQR27Zt4/333wdg1qxZhIWF0bJlS9zc3FiyZAmhoaFUr17dGU0hIn+UISJSyWVlZRnPPfecERMTYwQGBhrVqlUzGjdubPztb38zMjMzDcMwjBUrVhgNGjQwPDw8jPr161uPXbVqldGhQwfDx8fHCAgIMNq1a2e899571v2A8c477xjdu3c3zGazUb9+fWPhwoXW/e+9957RsmVLw9fX1wgICDC6detm7Ny502HXLiLXlkZ1iYhLK2k0mIhcv9THR0RERFyGgo+IiIi4DHVuFhGXprf9Iq5FT3xERETEZSj4iIiIiMtQ8BERERGXoeAjIiIiLkPBR0RERFyGgo+IiIi4DAUfERERcRkKPiIiIuIyFHxERETEZfw/lxJI8xcbOqwAAAAASUVORK5CYII=\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", "text/plain": [ "
    " ] @@ -304,8 +327,10 @@ }, { "cell_type": "markdown", - "id": "967f86f4", - "metadata": {}, + "id": "9defc5e7", + "metadata": { + "editable": true + }, "source": [ "## Building a tree, regression\n", "\n", @@ -324,8 +349,10 @@ }, { "cell_type": "markdown", - "id": "58c89f85", - "metadata": {}, + "id": "97bfeb0f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\sum_{j=1}^J\\sum_{i\\in R_j}(y_i-\\overline{y}_{R_j})^2,\n", @@ -334,8 +361,10 @@ }, { "cell_type": "markdown", - "id": "8d28defb", - "metadata": {}, + "id": "60e3b6f5", + "metadata": { + "editable": true + }, "source": [ "where $\\overline{y}_{R_j}$ is the mean response for the training observations \n", "within box $j$." @@ -343,8 +372,10 @@ }, { "cell_type": "markdown", - "id": "3f6c36d6", - "metadata": {}, + "id": "6a950a77", + "metadata": { + "editable": true + }, "source": [ "## A top-down approach, recursive binary splitting\n", "\n", @@ -363,8 +394,10 @@ }, { "cell_type": "markdown", - "id": "a89a52db", - "metadata": {}, + "id": "b7aa91ac", + "metadata": { + "editable": true + }, "source": [ "## Making a tree\n", "\n", @@ -374,8 +407,10 @@ }, { "cell_type": "markdown", - "id": "5feb6c75", - "metadata": {}, + "id": "ecfe94f2", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\left\\{X\\vert x_j < s\\right\\},\n", @@ -384,16 +419,20 @@ }, { "cell_type": "markdown", - "id": "8de30cae", - "metadata": {}, + "id": "b4970af3", + "metadata": { + "editable": true + }, "source": [ "and" ] }, { "cell_type": "markdown", - "id": "95d5c167", - "metadata": {}, + "id": "85bd3a40", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\left\\{X\\vert x_j \\geq s\\right\\},\n", @@ -402,16 +441,20 @@ }, { "cell_type": "markdown", - "id": "c5f10599", - "metadata": {}, + "id": "1ea5b535", + "metadata": { + "editable": true + }, "source": [ "so that we obtain the lowest MSE, that is" ] }, { "cell_type": "markdown", - "id": "ff6f03cb", - "metadata": {}, + "id": "b5672b44", + "metadata": { + "editable": true + }, "source": [ "$$\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", @@ -420,8 +463,10 @@ }, { "cell_type": "markdown", - "id": "9f031abb", - "metadata": {}, + "id": "999be0b7", + "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", @@ -451,8 +496,10 @@ }, { "cell_type": "markdown", - "id": "83f9c272", - "metadata": {}, + "id": "ac6a5d56", + "metadata": { + "editable": true + }, "source": [ "## Pruning the tree\n", "\n", @@ -473,8 +520,10 @@ }, { "cell_type": "markdown", - "id": "9f23f5ac", - "metadata": {}, + "id": "63deb833", + "metadata": { + "editable": true + }, "source": [ "## Cost complexity pruning\n", "\n", @@ -483,8 +532,10 @@ }, { "cell_type": "markdown", - "id": "a9b2646e", - "metadata": {}, + "id": "9f4c860f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\sum_{m=1}^{\\overline{T}}\\sum_{i:x_i\\in R_m}(y_i-\\overline{y}_{R_m})^2+\\alpha\\overline{T},\n", @@ -493,8 +544,10 @@ }, { "cell_type": "markdown", - "id": "8c9f1038", - "metadata": {}, + "id": "fd542b6e", + "metadata": { + "editable": true + }, "source": [ "is as small as possible. Here $\\overline{T}$ is \n", "the number of terminal nodes of the tree $T$ , $R_m$ is the\n", @@ -519,8 +572,10 @@ }, { "cell_type": "markdown", - "id": "1e4cf9ca", - "metadata": {}, + "id": "c5099f78", + "metadata": { + "editable": true + }, "source": [ "## Schematic Regression Procedure\n", "\n", @@ -543,8 +598,10 @@ }, { "cell_type": "markdown", - "id": "328198af", - "metadata": {}, + "id": "88e2ce13", + "metadata": { + "editable": true + }, "source": [ "## A Classification Tree\n", "\n", @@ -564,8 +621,10 @@ }, { "cell_type": "markdown", - "id": "338052bc", - "metadata": {}, + "id": "9fab855d", + "metadata": { + "editable": true + }, "source": [ "## Growing a classification tree\n", "\n", @@ -589,8 +648,10 @@ }, { "cell_type": "markdown", - "id": "c96ca06b", - "metadata": {}, + "id": "8dc57784", + "metadata": { + "editable": true + }, "source": [ "## Classification tree, how to split nodes\n", "\n", @@ -606,8 +667,10 @@ }, { "cell_type": "markdown", - "id": "970d1672", - "metadata": {}, + "id": "3a23d427", + "metadata": { + "editable": true + }, "source": [ "$$\n", "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i=k).\n", @@ -616,8 +679,10 @@ }, { "cell_type": "markdown", - "id": "e5a9ac3c", - "metadata": {}, + "id": "78e66687", + "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", @@ -627,8 +692,10 @@ }, { "cell_type": "markdown", - "id": "a0e10726", - "metadata": {}, + "id": "9157055f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i\\ne k) = 1-p_{mk}.\n", @@ -637,16 +704,20 @@ }, { "cell_type": "markdown", - "id": "f73a5a66", - "metadata": {}, + "id": "259d56c7", + "metadata": { + "editable": true + }, "source": [ "* Gini index $g$" ] }, { "cell_type": "markdown", - "id": "c6e5ec5f", - "metadata": {}, + "id": "66f8ee1a", + "metadata": { + "editable": true + }, "source": [ "$$\n", "g = \\sum_{k=1}^K p_{mk}(1-p_{mk}).\n", @@ -655,16 +726,20 @@ }, { "cell_type": "markdown", - "id": "c50e4c55", - "metadata": {}, + "id": "7478d6d0", + "metadata": { + "editable": true + }, "source": [ "* Information entropy or just entropy $s$" ] }, { "cell_type": "markdown", - "id": "34f4deed", - "metadata": {}, + "id": "30737ebf", + "metadata": { + "editable": true + }, "source": [ "$$\n", "s = -\\sum_{k=1}^K p_{mk}\\log{p_{mk}}.\n", @@ -673,8 +748,10 @@ }, { "cell_type": "markdown", - "id": "b2f791b8", - "metadata": {}, + "id": "c145a6a4", + "metadata": { + "editable": true + }, "source": [ "## Visualizing the Tree, Classification" ] @@ -682,8 +759,11 @@ { "cell_type": "code", "execution_count": 2, - "id": "b373a31c", - "metadata": {}, + "id": "c04064d0", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -833,8 +913,10 @@ }, { "cell_type": "markdown", - "id": "f1649fd9", - "metadata": {}, + "id": "da6433b2", + "metadata": { + "editable": true + }, "source": [ "## Visualizing the Tree, The Moons" ] @@ -842,8 +924,11 @@ { "cell_type": "code", "execution_count": 3, - "id": "63e625c0", - "metadata": {}, + "id": "7a884220", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "data": { @@ -885,8 +970,10 @@ }, { "cell_type": "markdown", - "id": "119ea0ae", - "metadata": {}, + "id": "4dfa7512", + "metadata": { + "editable": true + }, "source": [ "## Other ways of visualizing the trees\n", "\n", @@ -896,8 +983,11 @@ { "cell_type": "code", "execution_count": 4, - "id": "711bdf8d", - "metadata": {}, + "id": "bc172ee1", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "data": { @@ -952,8 +1042,10 @@ }, { "cell_type": "markdown", - "id": "a80a7f27", - "metadata": {}, + "id": "b7996538", + "metadata": { + "editable": true + }, "source": [ "## Printing out as text\n", "\n", @@ -964,8 +1056,11 @@ { "cell_type": "code", "execution_count": 5, - "id": "3aa4b27b", - "metadata": {}, + "id": "18c69627", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -995,8 +1090,10 @@ }, { "cell_type": "markdown", - "id": "e155d65a", - "metadata": {}, + "id": "1d734081", + "metadata": { + "editable": true + }, "source": [ "## Algorithms for Setting up Decision Trees\n", "\n", @@ -1013,8 +1110,10 @@ }, { "cell_type": "markdown", - "id": "9f64d255", - "metadata": {}, + "id": "17e0e2f9", + "metadata": { + "editable": true + }, "source": [ "## The CART algorithm for Classification\n", "\n", @@ -1028,8 +1127,10 @@ }, { "cell_type": "markdown", - "id": "c67ea6bd", - "metadata": {}, + "id": "73fc933b", + "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", @@ -1038,8 +1139,10 @@ }, { "cell_type": "markdown", - "id": "60be0c2f", - "metadata": {}, + "id": "f8ca9923", + "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", @@ -1053,8 +1156,10 @@ }, { "cell_type": "markdown", - "id": "68adc691", - "metadata": {}, + "id": "feb6fee3", + "metadata": { + "editable": true + }, "source": [ "## The CART algorithm for Regression\n", "\n", @@ -1064,8 +1169,10 @@ }, { "cell_type": "markdown", - "id": "3aa84faa", - "metadata": {}, + "id": "24b0aea3", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}\\mathrm{MSE}_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}\\mathrm{MSE}_{\\mathrm{right}}.\n", @@ -1074,16 +1181,20 @@ }, { "cell_type": "markdown", - "id": "05821fe6", - "metadata": {}, + "id": "8a01610a", + "metadata": { + "editable": true + }, "source": [ "Here the MSE for a specific node is defined as" ] }, { "cell_type": "markdown", - "id": "321fb878", - "metadata": {}, + "id": "9c28e4ab", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{MSE}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}(\\overline{y}_{\\mathrm{node}}-y_i)^2,\n", @@ -1092,16 +1203,20 @@ }, { "cell_type": "markdown", - "id": "5703ea44", - "metadata": {}, + "id": "76dc3641", + "metadata": { + "editable": true + }, "source": [ "with" ] }, { "cell_type": "markdown", - "id": "6b6cf145", - "metadata": {}, + "id": "60f57c10", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\overline{y}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}y_i,\n", @@ -1110,8 +1225,10 @@ }, { "cell_type": "markdown", - "id": "57f159ee", - "metadata": {}, + "id": "8c3e3601", + "metadata": { + "editable": true + }, "source": [ "the mean value of all observations in a specific node.\n", "\n", @@ -1121,8 +1238,10 @@ }, { "cell_type": "markdown", - "id": "8dba6c9f", - "metadata": {}, + "id": "0be42f06", + "metadata": { + "editable": true + }, "source": [ "## Why binary splits?\n", "\n", @@ -1134,8 +1253,10 @@ }, { "cell_type": "markdown", - "id": "54686dd2", - "metadata": {}, + "id": "42ed666b", + "metadata": { + "editable": true + }, "source": [ "## Computing a Tree using the Gini Index\n", "\n", @@ -1158,8 +1279,10 @@ }, { "cell_type": "markdown", - "id": "226714bc", - "metadata": {}, + "id": "4ab2f2b9", + "metadata": { + "editable": true + }, "source": [ "## The Table\n", "\n", @@ -1184,8 +1307,10 @@ }, { "cell_type": "markdown", - "id": "132a6df7", - "metadata": {}, + "id": "df7b209e", + "metadata": { + "editable": true + }, "source": [ "## Computing the various Gini Indices\n", "\n", @@ -1199,8 +1324,10 @@ }, { "cell_type": "markdown", - "id": "75ab3e53", - "metadata": {}, + "id": "0f77f79d", + "metadata": { + "editable": true + }, "source": [ "## Computing the various Gini Indices, Hours slept\n", "\n", @@ -1211,8 +1338,10 @@ }, { "cell_type": "markdown", - "id": "be9d82ec", - "metadata": {}, + "id": "737f4e14", + "metadata": { + "editable": true + }, "source": [ "## Computing the various Gini Indices, Hours studied\n", "\n", @@ -1225,8 +1354,10 @@ }, { "cell_type": "markdown", - "id": "b502bb89", - "metadata": {}, + "id": "e2c139d1", + "metadata": { + "editable": true + }, "source": [ "## A possible code using Scikit-Learn" ] @@ -1234,8 +1365,11 @@ { "cell_type": "code", "execution_count": 6, - "id": "2e5fc857", - "metadata": {}, + "id": "d0089709", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "data": { @@ -1449,8 +1583,10 @@ }, { "cell_type": "markdown", - "id": "fe2aa246", - "metadata": {}, + "id": "5f185aef", + "metadata": { + "editable": true + }, "source": [ "## Further example: Computing the Gini index\n", "\n", @@ -1491,8 +1627,10 @@ }, { "cell_type": "markdown", - "id": "46f289da", - "metadata": {}, + "id": "fe73a991", + "metadata": { + "editable": true + }, "source": [ "## Simple Python Code to read in Data and perform Classification" ] @@ -1500,8 +1638,11 @@ { "cell_type": "code", "execution_count": 7, - "id": "38aedbca", - "metadata": {}, + "id": "9b082c47", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -1644,8 +1785,10 @@ }, { "cell_type": "markdown", - "id": "a6f5da59", - "metadata": {}, + "id": "df9287bb", + "metadata": { + "editable": true + }, "source": [ "## Computing the Gini Factor\n", "\n", @@ -1660,8 +1803,11 @@ { "cell_type": "code", "execution_count": 8, - "id": "e51855f9", - "metadata": {}, + "id": "00d95a16", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -1792,8 +1938,10 @@ }, { "cell_type": "markdown", - "id": "f6add3e5", - "metadata": {}, + "id": "b6452b51", + "metadata": { + "editable": true + }, "source": [ "## Regression trees" ] @@ -1801,8 +1949,11 @@ { "cell_type": "code", "execution_count": 9, - "id": "74ecc649", - "metadata": {}, + "id": "3a98b310", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# Quadratic training set + noise\n", @@ -1816,8 +1967,11 @@ { "cell_type": "code", "execution_count": 10, - "id": "04024d89", - "metadata": {}, + "id": "1f8e183f", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "data": { @@ -1839,8 +1993,10 @@ }, { "cell_type": "markdown", - "id": "878b4d23", - "metadata": {}, + "id": "6c91981e", + "metadata": { + "editable": true + }, "source": [ "## Final regressor code" ] @@ -1848,8 +2004,11 @@ { "cell_type": "code", "execution_count": 11, - "id": "3c96bff5", - "metadata": {}, + "id": "c9b69f54", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "data": { @@ -1910,8 +2069,11 @@ { "cell_type": "code", "execution_count": 12, - "id": "527b27ca", - "metadata": {}, + "id": "53db8f73", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "data": { @@ -1961,8 +2123,10 @@ }, { "cell_type": "markdown", - "id": "f2a0dd48", - "metadata": {}, + "id": "3be38ddf", + "metadata": { + "editable": true + }, "source": [ "## Pros and cons of trees, pros\n", "\n", @@ -1983,8 +2147,10 @@ }, { "cell_type": "markdown", - "id": "9f896560", - "metadata": {}, + "id": "e4aaab5f", + "metadata": { + "editable": true + }, "source": [ "## Disadvantages\n", "\n", @@ -2009,8 +2175,10 @@ }, { "cell_type": "markdown", - "id": "3f6f50e2", - "metadata": {}, + "id": "0010cb58", + "metadata": { + "editable": true + }, "source": [ "## Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods\n", "\n", @@ -2038,8 +2206,10 @@ }, { "cell_type": "markdown", - "id": "24509012", - "metadata": {}, + "id": "f3a71c11", + "metadata": { + "editable": true + }, "source": [ "## An Overview of Ensemble Methods\n", "\n", @@ -2052,8 +2222,10 @@ }, { "cell_type": "markdown", - "id": "15a871bc", - "metadata": {}, + "id": "aef5e772", + "metadata": { + "editable": true + }, "source": [ "## Why Voting?\n", "\n", @@ -2074,8 +2246,10 @@ }, { "cell_type": "markdown", - "id": "e6d75533", - "metadata": {}, + "id": "a30b02f8", + "metadata": { + "editable": true + }, "source": [ "## Tossing coins\n", "\n", @@ -2103,8 +2277,10 @@ }, { "cell_type": "markdown", - "id": "8ecb23d0", - "metadata": {}, + "id": "3afd02ed", + "metadata": { + "editable": true + }, "source": [ "## Standard imports first" ] @@ -2112,8 +2288,11 @@ { "cell_type": "code", "execution_count": 13, - "id": "b42d0a08", - "metadata": {}, + "id": "4a1b7a89", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# Common imports\n", @@ -2157,8 +2336,10 @@ }, { "cell_type": "markdown", - "id": "e3060cfd", - "metadata": {}, + "id": "0c32e96d", + "metadata": { + "editable": true + }, "source": [ "## Simple Voting Example, head or tail" ] @@ -2166,8 +2347,11 @@ { "cell_type": "code", "execution_count": 14, - "id": "59d25264", - "metadata": {}, + "id": "4d1c99f7", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "data": { @@ -2212,8 +2396,10 @@ }, { "cell_type": "markdown", - "id": "f8cf0e6e", - "metadata": {}, + "id": "e0e3f1dc", + "metadata": { + "editable": true + }, "source": [ "## Using the Voting Classifier\n", "\n", @@ -2223,8 +2409,11 @@ { "cell_type": "code", "execution_count": 15, - "id": "76fd4c2d", - "metadata": {}, + "id": "c3d00f7d", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -2288,8 +2477,10 @@ }, { "cell_type": "markdown", - "id": "eacefe6c", - "metadata": {}, + "id": "f0d12f2c", + "metadata": { + "editable": true + }, "source": [ "## Voting and Bagging" ] @@ -2297,8 +2488,11 @@ { "cell_type": "code", "execution_count": 16, - "id": "796dfa6b", - "metadata": {}, + "id": "1bbde2a9", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "data": { @@ -2337,8 +2531,11 @@ { "cell_type": "code", "execution_count": 17, - "id": "90ec162f", - "metadata": {}, + "id": "80a82744", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -2369,8 +2566,11 @@ { "cell_type": "code", "execution_count": 18, - "id": "e46dcb77", - "metadata": {}, + "id": "65c732b4", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "data": { @@ -2400,8 +2600,11 @@ { "cell_type": "code", "execution_count": 19, - "id": "67a3b080", - "metadata": {}, + "id": "956f7ec5", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -2431,8 +2634,10 @@ }, { "cell_type": "markdown", - "id": "f0d51672", - "metadata": {}, + "id": "ee527167", + "metadata": { + "editable": true + }, "source": [ "## Bagging\n", "\n", @@ -2451,8 +2656,10 @@ }, { "cell_type": "markdown", - "id": "ab182ea8", - "metadata": {}, + "id": "354baed9", + "metadata": { + "editable": true + }, "source": [ "## More bagging\n", "\n", @@ -2481,8 +2688,10 @@ }, { "cell_type": "markdown", - "id": "998512be", - "metadata": {}, + "id": "fc1a2451", + "metadata": { + "editable": true + }, "source": [ "## Making your own Bootstrap: Changing the Level of the Decision Tree\n", "\n", @@ -2493,8 +2702,11 @@ { "cell_type": "code", "execution_count": 20, - "id": "6ac20f8b", - "metadata": {}, + "id": "129bb9fb", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -2622,8 +2834,10 @@ }, { "cell_type": "markdown", - "id": "c9a44ff4", - "metadata": {}, + "id": "170b00ab", + "metadata": { + "editable": true + }, "source": [ "## Random forests\n", "\n", @@ -2643,8 +2857,10 @@ }, { "cell_type": "markdown", - "id": "74f9056f", - "metadata": {}, + "id": "58a4aa65", + "metadata": { + "editable": true + }, "source": [ "$$\n", "m\\approx \\sqrt{p}.\n", @@ -2653,8 +2869,10 @@ }, { "cell_type": "markdown", - "id": "9a0166e1", - "metadata": {}, + "id": "a5ed07c1", + "metadata": { + "editable": true + }, "source": [ "In building a random forest, at\n", "each split in the tree, the algorithm is not even allowed to consider\n", @@ -2676,8 +2894,10 @@ }, { "cell_type": "markdown", - "id": "7e5dd3c9", - "metadata": {}, + "id": "c5369edb", + "metadata": { + "editable": true + }, "source": [ "## Random Forest Algorithm\n", "The algorithm described here can be applied to both classification and regression problems.\n", @@ -2700,8 +2920,10 @@ }, { "cell_type": "markdown", - "id": "b2476d94", - "metadata": {}, + "id": "4355c5ad", + "metadata": { + "editable": true + }, "source": [ "## Random Forests Compared with other Methods on the Cancer Data" ] @@ -2709,8 +2931,11 @@ { "cell_type": "code", "execution_count": 21, - "id": "0a56d5de", - "metadata": {}, + "id": "0fbcd18f", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -2841,8 +3066,10 @@ }, { "cell_type": "markdown", - "id": "ef32420e", - "metadata": {}, + "id": "4072de62", + "metadata": { + "editable": true + }, "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", @@ -2855,8 +3082,10 @@ }, { "cell_type": "markdown", - "id": "5820ebfd", - "metadata": {}, + "id": "481673fb", + "metadata": { + "editable": true + }, "source": [ "## Compare Bagging on Trees with Random Forests" ] @@ -2864,8 +3093,11 @@ { "cell_type": "code", "execution_count": 22, - "id": "bb5bea62", - "metadata": {}, + "id": "d5fe5939", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "bag_clf = BaggingClassifier(\n", @@ -2876,8 +3108,11 @@ { "cell_type": "code", "execution_count": 23, - "id": "b879f3ce", - "metadata": {}, + "id": "7ee6160d", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "data": { @@ -2902,8 +3137,10 @@ }, { "cell_type": "markdown", - "id": "60160f97", - "metadata": {}, + "id": "3a6e484c", + "metadata": { + "editable": true + }, "source": [ "## Boosting, a Bird's Eye View\n", "\n", @@ -2920,8 +3157,10 @@ }, { "cell_type": "markdown", - "id": "b351b1bd", - "metadata": {}, + "id": "ed91ea28", + "metadata": { + "editable": true + }, "source": [ "## What is boosting? Additive Modelling/Iterative Fitting\n", "\n", @@ -2932,8 +3171,10 @@ }, { "cell_type": "markdown", - "id": "6e9174ef", - "metadata": {}, + "id": "9ab6f212", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n", @@ -2942,8 +3183,10 @@ }, { "cell_type": "markdown", - "id": "fc319721", - "metadata": {}, + "id": "288a57f1", + "metadata": { + "editable": true + }, "source": [ "where $\\beta_m$ are the expansion parameters to be determined in a\n", "minimization process and $b(x;\\gamma_m)$ are some simple functions of\n", @@ -2957,8 +3200,10 @@ }, { "cell_type": "markdown", - "id": "da4ba861", - "metadata": {}, + "id": "d0ea7a14", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\sigma(t) = \\frac{1}{1+\\exp{(-t)}},\n", @@ -2967,8 +3212,10 @@ }, { "cell_type": "markdown", - "id": "f444a5a4", - "metadata": {}, + "id": "faa1b446", + "metadata": { + "editable": true + }, "source": [ "where $t=\\gamma_0+\\gamma_1 x$ and the parameters $\\gamma_0$ and\n", "$\\gamma_1$ were determined by the Logistic Regression fitting\n", @@ -2979,8 +3226,10 @@ }, { "cell_type": "markdown", - "id": "8a4d8175", - "metadata": {}, + "id": "a20c6de5", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(\\boldsymbol{y},\\boldsymbol{f}) = \\frac{1}{n} \\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n", @@ -2989,8 +3238,10 @@ }, { "cell_type": "markdown", - "id": "de12bc14", - "metadata": {}, + "id": "17800601", + "metadata": { + "editable": true + }, "source": [ "In this case the function $f(x)$ was replaced by the design matrix\n", "$\\boldsymbol{X}$ and the unknown linear regression parameters $\\boldsymbol{\\beta}$,\n", @@ -3000,8 +3251,10 @@ }, { "cell_type": "markdown", - "id": "735bf417", - "metadata": {}, + "id": "7b59e224", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\boldsymbol{\\beta}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n", @@ -3010,16 +3263,20 @@ }, { "cell_type": "markdown", - "id": "22b8d82f", - "metadata": {}, + "id": "f5917a9a", + "metadata": { + "editable": true + }, "source": [ "In iterative fitting or additive modeling, we minimize the cost function with respect to the parameters $\\beta_m$ and $\\gamma_m$." ] }, { "cell_type": "markdown", - "id": "661db2e1", - "metadata": {}, + "id": "8d2101f2", + "metadata": { + "editable": true + }, "source": [ "## Iterative Fitting, Regression and Squared-error Cost Function\n", "\n", @@ -3044,8 +3301,10 @@ }, { "cell_type": "markdown", - "id": "2b14c81e", - "metadata": {}, + "id": "65f77895", + "metadata": { + "editable": true + }, "source": [ "## Squared-Error Example and Iterative Fitting\n", "\n", @@ -3058,8 +3317,10 @@ }, { "cell_type": "markdown", - "id": "64c44231", - "metadata": {}, + "id": "28b76851", + "metadata": { + "editable": true + }, "source": [ "$$\n", "(\\beta_m,\\gamma_m) = \\mathrm{argmin}_{\\beta,\\lambda}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta b(x;\\gamma))^2=\\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta(1+\\gamma x_i))^2.\n", @@ -3068,8 +3329,10 @@ }, { "cell_type": "markdown", - "id": "1040bdaf", - "metadata": {}, + "id": "985ccfb2", + "metadata": { + "editable": true + }, "source": [ "We start our iteration by simply setting $f_0(x)=0$. \n", "Taking the derivatives with respect to $\\beta$ and $\\gamma$ we obtain" @@ -3077,8 +3340,10 @@ }, { "cell_type": "markdown", - "id": "de59d269", - "metadata": {}, + "id": "05d75812", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial {\\cal C}}{\\partial \\beta} = -2\\sum_{i}(1+\\gamma x_i)(y_i-\\beta(1+\\gamma x_i))=0,\n", @@ -3087,16 +3352,20 @@ }, { "cell_type": "markdown", - "id": "5f87e844", - "metadata": {}, + "id": "9cfc5c74", + "metadata": { + "editable": true + }, "source": [ "and" ] }, { "cell_type": "markdown", - "id": "a2f9215c", - "metadata": {}, + "id": "3833eb7b", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial {\\cal C}}{\\partial \\gamma} =-2\\sum_{i}\\beta x_i(y_i-\\beta(1+\\gamma x_i))=0.\n", @@ -3105,16 +3374,20 @@ }, { "cell_type": "markdown", - "id": "67f71f90", - "metadata": {}, + "id": "3fca374c", + "metadata": { + "editable": true + }, "source": [ "We can then rewrite these equations as (defining $\\boldsymbol{w}=\\boldsymbol{e}+\\gamma \\boldsymbol{x})$ with $\\boldsymbol{e}$ being the unit vector)" ] }, { "cell_type": "markdown", - "id": "5410f260", - "metadata": {}, + "id": "aeb623d7", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\gamma \\boldsymbol{w}^T(\\boldsymbol{y}-\\beta\\gamma \\boldsymbol{w})=0,\n", @@ -3123,16 +3396,20 @@ }, { "cell_type": "markdown", - "id": "0485a1f5", - "metadata": {}, + "id": "f8e542a2", + "metadata": { + "editable": true + }, "source": [ "which gives us $\\beta = \\boldsymbol{w}^T\\boldsymbol{y}/(\\boldsymbol{w}^T\\boldsymbol{w})$. Similarly we have" ] }, { "cell_type": "markdown", - "id": "3a256711", - "metadata": {}, + "id": "d92e3136", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\beta\\gamma \\boldsymbol{x}^T(\\boldsymbol{y}-\\beta(1+\\gamma \\boldsymbol{x}))=0,\n", @@ -3141,8 +3418,10 @@ }, { "cell_type": "markdown", - "id": "fc8cd2ae", - "metadata": {}, + "id": "bca4d27a", + "metadata": { + "editable": true + }, "source": [ "which leads to $\\gamma =(\\boldsymbol{x}^T\\boldsymbol{y}-\\beta\\boldsymbol{x}^T\\boldsymbol{e})/(\\beta\\boldsymbol{x}^T\\boldsymbol{x})$. Inserting\n", "for $\\beta$ gives us an equation for $\\gamma$. This is a non-linear equation in the unknown $\\gamma$ and has to be solved numerically. \n", @@ -3153,8 +3432,10 @@ }, { "cell_type": "markdown", - "id": "0a9ecf4b", - "metadata": {}, + "id": "d2fa1316", + "metadata": { + "editable": true + }, "source": [ "## Iterative Fitting, Classification and AdaBoost\n", "\n", @@ -3167,8 +3448,10 @@ }, { "cell_type": "markdown", - "id": "ac605ae7", - "metadata": {}, + "id": "5401b686", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{\\overline{err}}=\\frac{1}{n} \\sum_{i=0}^{n-1} I(y_i\\ne G(x_i)).\n", @@ -3177,8 +3460,10 @@ }, { "cell_type": "markdown", - "id": "b4d530db", - "metadata": {}, + "id": "082464c4", + "metadata": { + "editable": true + }, "source": [ "The iterative procedure starts with defining a weak classifier whose\n", "error rate is barely better than random guessing. The iterative\n", @@ -3191,8 +3476,10 @@ }, { "cell_type": "markdown", - "id": "0f9fce0f", - "metadata": {}, + "id": "4f2a4a17", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n", @@ -3201,16 +3488,20 @@ }, { "cell_type": "markdown", - "id": "73471c17", - "metadata": {}, + "id": "4aad349a", + "metadata": { + "editable": true + }, "source": [ "will be a function of" ] }, { "cell_type": "markdown", - "id": "d8244842", - "metadata": {}, + "id": "d2050bb1", + "metadata": { + "editable": true + }, "source": [ "$$\n", "G_M(x) = \\mathrm{sign} \\sum_{i=1}^M \\alpha_m G_m(x).\n", @@ -3219,8 +3510,10 @@ }, { "cell_type": "markdown", - "id": "be09fe99", - "metadata": {}, + "id": "359b0eb3", + "metadata": { + "editable": true + }, "source": [ "## Adaptive Boosting, AdaBoost\n", "\n", @@ -3229,8 +3522,10 @@ }, { "cell_type": "markdown", - "id": "a547cf77", - "metadata": {}, + "id": "eab77ff9", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_m(x) = f_{m-1}(x)+\\beta_mG_m(x).\n", @@ -3239,8 +3534,10 @@ }, { "cell_type": "markdown", - "id": "67b1198a", - "metadata": {}, + "id": "d7c87ec5", + "metadata": { + "editable": true + }, "source": [ "The simplest possible cost function which leads (also simple from a computational point of view) to the AdaBoost algorithm is the\n", "exponential cost/loss function defined as" @@ -3248,8 +3545,10 @@ }, { "cell_type": "markdown", - "id": "f0a75e83", - "metadata": {}, + "id": "3cf53a5f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}\\exp{(-y_i(f_{m-1}(x_i)+\\beta G(x_i))}.\n", @@ -3258,8 +3557,10 @@ }, { "cell_type": "markdown", - "id": "9d2d96dc", - "metadata": {}, + "id": "67bbb3ac", + "metadata": { + "editable": true + }, "source": [ "We optimize $\\beta$ and $G$ for each value of $m=1:M$ as we did in the regression case.\n", "This is normally done in two steps. Let us however first rewrite the cost function as" @@ -3267,8 +3568,10 @@ }, { "cell_type": "markdown", - "id": "a6c2a558", - "metadata": {}, + "id": "d9a2448e", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}w_i^{m}\\exp{(-y_i\\beta G(x_i))},\n", @@ -3277,16 +3580,20 @@ }, { "cell_type": "markdown", - "id": "5a582df6", - "metadata": {}, + "id": "392f16f2", + "metadata": { + "editable": true + }, "source": [ "where we have defined $w_i^m= \\exp{(-y_if_{m-1}(x_i))}$." ] }, { "cell_type": "markdown", - "id": "654c5f13", - "metadata": {}, + "id": "a645e83c", + "metadata": { + "editable": true + }, "source": [ "## Building up AdaBoost\n", "\n", @@ -3295,8 +3602,10 @@ }, { "cell_type": "markdown", - "id": "efecb2bc", - "metadata": {}, + "id": "89e1d11e", + "metadata": { + "editable": true + }, "source": [ "$$\n", "G_m(x) = \\mathrm{sign} \\sum_{i=0}^{n-1} w_i^m I(y_i \\ne G_(x_i)),\n", @@ -3305,8 +3614,10 @@ }, { "cell_type": "markdown", - "id": "da78bb27", - "metadata": {}, + "id": "f884f217", + "metadata": { + "editable": true + }, "source": [ "which is the classifier that minimizes the weighted error rate in predicting $y$.\n", "\n", @@ -3315,8 +3626,10 @@ }, { "cell_type": "markdown", - "id": "dc1c118f", - "metadata": {}, + "id": "664acf19", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\exp{-(\\beta)}\\sum_{y_i=G(x_i)}w_i^m+\\exp{(\\beta)}\\sum_{y_i\\ne G(x_i)}w_i^m,\n", @@ -3325,16 +3638,20 @@ }, { "cell_type": "markdown", - "id": "1b77640d", - "metadata": {}, + "id": "d83ebe5e", + "metadata": { + "editable": true + }, "source": [ "which can be rewritten as" ] }, { "cell_type": "markdown", - "id": "d7944742", - "metadata": {}, + "id": "96134017", + "metadata": { + "editable": true + }, "source": [ "$$\n", "(\\exp{(\\beta)}-\\exp{-(\\beta)})\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i))+\\exp{(-\\beta)}\\sum_{i=0}^{n-1}w_i^m=0,\n", @@ -3343,16 +3660,20 @@ }, { "cell_type": "markdown", - "id": "94ffa0c4", - "metadata": {}, + "id": "97fc79c8", + "metadata": { + "editable": true + }, "source": [ "which leads to" ] }, { "cell_type": "markdown", - "id": "eae46622", - "metadata": {}, + "id": "0d1e4f3e", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\beta_m = \\frac{1}{2}\\log{\\frac{1-\\mathrm{\\overline{err}}}{\\mathrm{\\overline{err}}}},\n", @@ -3361,16 +3682,20 @@ }, { "cell_type": "markdown", - "id": "099f71b5", - "metadata": {}, + "id": "7d571a24", + "metadata": { + "editable": true + }, "source": [ "where we have redefined the error as" ] }, { "cell_type": "markdown", - "id": "11e5f200", - "metadata": {}, + "id": "8f31a3a7", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{\\overline{err}}_m=\\frac{1}{n}\\frac{\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i)}{\\sum_{i=0}^{n-1}w_i^m},\n", @@ -3379,16 +3704,20 @@ }, { "cell_type": "markdown", - "id": "77b52ed2", - "metadata": {}, + "id": "e0e3db77", + "metadata": { + "editable": true + }, "source": [ "which leads to an update of" ] }, { "cell_type": "markdown", - "id": "e8fe5df6", - "metadata": {}, + "id": "fc1ce185", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_m(x) = f_{m-1}(x) +\\beta_m G_m(x).\n", @@ -3397,16 +3726,20 @@ }, { "cell_type": "markdown", - "id": "4c1ea9b7", - "metadata": {}, + "id": "d7360d72", + "metadata": { + "editable": true + }, "source": [ "This leads to the new weights" ] }, { "cell_type": "markdown", - "id": "a61b875a", - "metadata": {}, + "id": "cf486320", + "metadata": { + "editable": true + }, "source": [ "$$\n", "w_i^{m+1} = w_i^m \\exp{(-y_i\\beta_m G_m(x_i))}\n", @@ -3415,8 +3748,10 @@ }, { "cell_type": "markdown", - "id": "a0df6e36", - "metadata": {}, + "id": "8ebdf8ce", + "metadata": { + "editable": true + }, "source": [ "## Adaptive boosting: AdaBoost, Basic Algorithm\n", "\n", @@ -3433,8 +3768,10 @@ }, { "cell_type": "markdown", - "id": "862806de", - "metadata": {}, + "id": "853a1218", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{err}=\\frac{1}{n}\\sum_{i=0}^{n-1}I(y_i\\ne G(x_i)),\n", @@ -3443,16 +3780,20 @@ }, { "cell_type": "markdown", - "id": "60c6b96e", - "metadata": {}, + "id": "d287b19d", + "metadata": { + "editable": true + }, "source": [ "where the function $I()$ is one if we misclassify and zero if we classify correctly." ] }, { "cell_type": "markdown", - "id": "d4cf16bb", - "metadata": {}, + "id": "637ef064", + "metadata": { + "editable": true + }, "source": [ "## Basic Steps of AdaBoost\n", "\n", @@ -3465,8 +3806,10 @@ }, { "cell_type": "markdown", - "id": "91e907b9", - "metadata": {}, + "id": "f1698dc3", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{\\overline{err}}_m=\\frac{\\sum_{i=0}^{n-1}w_i^m I(y_i\\ne G(x_i))}{\\sum_{i=0}^{n-1}w_i},\n", @@ -3475,8 +3818,10 @@ }, { "cell_type": "markdown", - "id": "cc913a38", - "metadata": {}, + "id": "c6d08c01", + "metadata": { + "editable": true + }, "source": [ "1. Then we start looping over all attempts at classifying, namely we start an iterative process for $m=1:M$, where $M$ is the final number of classifications. Our given classifier could for example be a plain decision tree.\n", "\n", @@ -3501,8 +3846,10 @@ }, { "cell_type": "markdown", - "id": "87e49535", - "metadata": {}, + "id": "c58db919", + "metadata": { + "editable": true + }, "source": [ "## AdaBoost Examples\n", "\n", @@ -3512,8 +3859,11 @@ { "cell_type": "code", "execution_count": 24, - "id": "a48ac6a2", - "metadata": {}, + "id": "104e1c7c", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "data": { @@ -3584,11 +3934,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, "language_info": { "codemirror_mode": { "name": "ipython", diff --git a/doc/LectureNotes/_build/jupyter_execute/week46.py b/doc/LectureNotes/_build/jupyter_execute/week46.py index a255e4c86..f4e10b228 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week46.py +++ b/doc/LectureNotes/_build/jupyter_execute/week46.py @@ -28,7 +28,9 @@ # # * These lecture notes # -# * [Video of lecture to be added](https://youtu.be/) +# * [Video of lecture](https://youtu.be/PMswUwhYa7k) +# +# * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov16.pdf) # # * [Video on Decision trees](https://www.youtube.com/watch?v=RmajweUFKvM&ab_channel=Simplilearn) # diff --git a/doc/LectureNotes/_build/jupyter_execute/week46_9_1.png b/doc/LectureNotes/_build/jupyter_execute/week46_9_1.png index 7f50abbd5..1e08215a0 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/week46_9_1.png and b/doc/LectureNotes/_build/jupyter_execute/week46_9_1.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week46_9_2.png b/doc/LectureNotes/_build/jupyter_execute/week46_9_2.png index b037f509f..3cf74c53c 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/week46_9_2.png and b/doc/LectureNotes/_build/jupyter_execute/week46_9_2.png differ diff --git a/doc/LectureNotes/week46.ipynb b/doc/LectureNotes/week46.ipynb index 0b23538ed..87bc2f83d 100644 --- a/doc/LectureNotes/week46.ipynb +++ b/doc/LectureNotes/week46.ipynb @@ -2,8 +2,10 @@ "cells": [ { "cell_type": "markdown", - "id": "f4d3b2c9", - "metadata": {}, + "id": "f9935da5", + "metadata": { + "editable": true + }, "source": [ "\n", @@ -12,8 +14,10 @@ }, { "cell_type": "markdown", - "id": "57cda95f", - "metadata": {}, + "id": "0b990b1c", + "metadata": { + "editable": true + }, "source": [ "# Week 46: 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", @@ -23,8 +27,10 @@ }, { "cell_type": "markdown", - "id": "0ab525ed", - "metadata": {}, + "id": "71e9f143", + "metadata": { + "editable": true + }, "source": [ "## Plan for week 46\n", "\n", @@ -44,7 +50,9 @@ "\n", " * These lecture notes\n", "\n", - " * [Video of lecture to be added](https://youtu.be/)\n", + " * [Video of lecture](https://youtu.be/PMswUwhYa7k)\n", + "\n", + " * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov16.pdf) \n", "\n", " * [Video on Decision trees](https://www.youtube.com/watch?v=RmajweUFKvM&ab_channel=Simplilearn)\n", "\n", @@ -53,8 +61,10 @@ }, { "cell_type": "markdown", - "id": "3c8e0d42", - "metadata": {}, + "id": "00b82c38", + "metadata": { + "editable": true + }, "source": [ "## Decision trees, overarching aims\n", "\n", @@ -82,8 +92,10 @@ }, { "cell_type": "markdown", - "id": "893c9b6f", - "metadata": {}, + "id": "c733808f", + "metadata": { + "editable": true + }, "source": [ "## Basics of a tree\n", "\n", @@ -100,8 +112,10 @@ }, { "cell_type": "markdown", - "id": "89b0fc63", - "metadata": {}, + "id": "2b60a24e", + "metadata": { + "editable": true + }, "source": [ "## A typical Decision Tree with its pertinent Jargon, Classification Problem\n", "\n", @@ -116,8 +130,10 @@ }, { "cell_type": "markdown", - "id": "d4354730", - "metadata": {}, + "id": "1a8dc5f7", + "metadata": { + "editable": true + }, "source": [ "## General Features\n", "\n", @@ -137,8 +153,10 @@ }, { "cell_type": "markdown", - "id": "9db7330a", - "metadata": {}, + "id": "188465ed", + "metadata": { + "editable": true + }, "source": [ "## How do we set it up?\n", "\n", @@ -158,8 +176,10 @@ }, { "cell_type": "markdown", - "id": "331ebf1d", - "metadata": {}, + "id": "7f927d1c", + "metadata": { + "editable": true + }, "source": [ "## Decision trees and Regression" ] @@ -167,40 +187,12 @@ { "cell_type": "code", "execution_count": 1, - "id": "122986df", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2nd degree coefficients:\n", - "zero power: 0.7163225806451621\n", - "first power: 0.17047319577389736\n", - "second power: -0.0006648714674365666\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "id": "92377283", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "%matplotlib inline\n", "\n", @@ -296,8 +288,10 @@ }, { "cell_type": "markdown", - "id": "967f86f4", - "metadata": {}, + "id": "9defc5e7", + "metadata": { + "editable": true + }, "source": [ "## Building a tree, regression\n", "\n", @@ -316,8 +310,10 @@ }, { "cell_type": "markdown", - "id": "58c89f85", - "metadata": {}, + "id": "97bfeb0f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\sum_{j=1}^J\\sum_{i\\in R_j}(y_i-\\overline{y}_{R_j})^2,\n", @@ -326,8 +322,10 @@ }, { "cell_type": "markdown", - "id": "8d28defb", - "metadata": {}, + "id": "60e3b6f5", + "metadata": { + "editable": true + }, "source": [ "where $\\overline{y}_{R_j}$ is the mean response for the training observations \n", "within box $j$." @@ -335,8 +333,10 @@ }, { "cell_type": "markdown", - "id": "3f6c36d6", - "metadata": {}, + "id": "6a950a77", + "metadata": { + "editable": true + }, "source": [ "## A top-down approach, recursive binary splitting\n", "\n", @@ -355,8 +355,10 @@ }, { "cell_type": "markdown", - "id": "a89a52db", - "metadata": {}, + "id": "b7aa91ac", + "metadata": { + "editable": true + }, "source": [ "## Making a tree\n", "\n", @@ -366,8 +368,10 @@ }, { "cell_type": "markdown", - "id": "5feb6c75", - "metadata": {}, + "id": "ecfe94f2", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\left\\{X\\vert x_j < s\\right\\},\n", @@ -376,16 +380,20 @@ }, { "cell_type": "markdown", - "id": "8de30cae", - "metadata": {}, + "id": "b4970af3", + "metadata": { + "editable": true + }, "source": [ "and" ] }, { "cell_type": "markdown", - "id": "95d5c167", - "metadata": {}, + "id": "85bd3a40", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\left\\{X\\vert x_j \\geq s\\right\\},\n", @@ -394,16 +402,20 @@ }, { "cell_type": "markdown", - "id": "c5f10599", - "metadata": {}, + "id": "1ea5b535", + "metadata": { + "editable": true + }, "source": [ "so that we obtain the lowest MSE, that is" ] }, { "cell_type": "markdown", - "id": "ff6f03cb", - "metadata": {}, + "id": "b5672b44", + "metadata": { + "editable": true + }, "source": [ "$$\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", @@ -412,8 +424,10 @@ }, { "cell_type": "markdown", - "id": "9f031abb", - "metadata": {}, + "id": "999be0b7", + "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", @@ -443,8 +457,10 @@ }, { "cell_type": "markdown", - "id": "83f9c272", - "metadata": {}, + "id": "ac6a5d56", + "metadata": { + "editable": true + }, "source": [ "## Pruning the tree\n", "\n", @@ -465,8 +481,10 @@ }, { "cell_type": "markdown", - "id": "9f23f5ac", - "metadata": {}, + "id": "63deb833", + "metadata": { + "editable": true + }, "source": [ "## Cost complexity pruning\n", "\n", @@ -475,8 +493,10 @@ }, { "cell_type": "markdown", - "id": "a9b2646e", - "metadata": {}, + "id": "9f4c860f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\sum_{m=1}^{\\overline{T}}\\sum_{i:x_i\\in R_m}(y_i-\\overline{y}_{R_m})^2+\\alpha\\overline{T},\n", @@ -485,8 +505,10 @@ }, { "cell_type": "markdown", - "id": "8c9f1038", - "metadata": {}, + "id": "fd542b6e", + "metadata": { + "editable": true + }, "source": [ "is as small as possible. Here $\\overline{T}$ is \n", "the number of terminal nodes of the tree $T$ , $R_m$ is the\n", @@ -511,8 +533,10 @@ }, { "cell_type": "markdown", - "id": "1e4cf9ca", - "metadata": {}, + "id": "c5099f78", + "metadata": { + "editable": true + }, "source": [ "## Schematic Regression Procedure\n", "\n", @@ -535,8 +559,10 @@ }, { "cell_type": "markdown", - "id": "328198af", - "metadata": {}, + "id": "88e2ce13", + "metadata": { + "editable": true + }, "source": [ "## A Classification Tree\n", "\n", @@ -556,8 +582,10 @@ }, { "cell_type": "markdown", - "id": "338052bc", - "metadata": {}, + "id": "9fab855d", + "metadata": { + "editable": true + }, "source": [ "## Growing a classification tree\n", "\n", @@ -581,8 +609,10 @@ }, { "cell_type": "markdown", - "id": "c96ca06b", - "metadata": {}, + "id": "8dc57784", + "metadata": { + "editable": true + }, "source": [ "## Classification tree, how to split nodes\n", "\n", @@ -598,8 +628,10 @@ }, { "cell_type": "markdown", - "id": "970d1672", - "metadata": {}, + "id": "3a23d427", + "metadata": { + "editable": true + }, "source": [ "$$\n", "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i=k).\n", @@ -608,8 +640,10 @@ }, { "cell_type": "markdown", - "id": "e5a9ac3c", - "metadata": {}, + "id": "78e66687", + "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", @@ -619,8 +653,10 @@ }, { "cell_type": "markdown", - "id": "a0e10726", - "metadata": {}, + "id": "9157055f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i\\ne k) = 1-p_{mk}.\n", @@ -629,16 +665,20 @@ }, { "cell_type": "markdown", - "id": "f73a5a66", - "metadata": {}, + "id": "259d56c7", + "metadata": { + "editable": true + }, "source": [ "* Gini index $g$" ] }, { "cell_type": "markdown", - "id": "c6e5ec5f", - "metadata": {}, + "id": "66f8ee1a", + "metadata": { + "editable": true + }, "source": [ "$$\n", "g = \\sum_{k=1}^K p_{mk}(1-p_{mk}).\n", @@ -647,16 +687,20 @@ }, { "cell_type": "markdown", - "id": "c50e4c55", - "metadata": {}, + "id": "7478d6d0", + "metadata": { + "editable": true + }, "source": [ "* Information entropy or just entropy $s$" ] }, { "cell_type": "markdown", - "id": "34f4deed", - "metadata": {}, + "id": "30737ebf", + "metadata": { + "editable": true + }, "source": [ "$$\n", "s = -\\sum_{k=1}^K p_{mk}\\log{p_{mk}}.\n", @@ -665,8 +709,10 @@ }, { "cell_type": "markdown", - "id": "b2f791b8", - "metadata": {}, + "id": "c145a6a4", + "metadata": { + "editable": true + }, "source": [ "## Visualizing the Tree, Classification" ] @@ -674,119 +720,12 @@ { "cell_type": "code", "execution_count": 2, - "id": "b373a31c", - "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" - } - ], + "id": "c04064d0", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "import os\n", "from sklearn.datasets import load_breast_cancer\n", @@ -825,8 +764,10 @@ }, { "cell_type": "markdown", - "id": "f1649fd9", - "metadata": {}, + "id": "da6433b2", + "metadata": { + "editable": true + }, "source": [ "## Visualizing the Tree, The Moons" ] @@ -834,20 +775,12 @@ { "cell_type": "code", "execution_count": 3, - "id": "63e625c0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "id": "7a884220", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "# Common imports\n", "import numpy as np\n", @@ -877,8 +810,10 @@ }, { "cell_type": "markdown", - "id": "119ea0ae", - "metadata": {}, + "id": "4dfa7512", + "metadata": { + "editable": true + }, "source": [ "## Other ways of visualizing the trees\n", "\n", @@ -888,46 +823,12 @@ { "cell_type": "code", "execution_count": 4, - "id": "711bdf8d", - "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] <= 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "id": "bc172ee1", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "from sklearn.datasets import load_iris\n", "from sklearn import tree\n", @@ -940,8 +841,10 @@ }, { "cell_type": "markdown", - "id": "a80a7f27", - "metadata": {}, + "id": "b7996538", + "metadata": { + "editable": true + }, "source": [ "## Printing out as text\n", "\n", @@ -952,24 +855,12 @@ { "cell_type": "code", "execution_count": 5, - "id": "3aa4b27b", - "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" - ] - } - ], + "id": "18c69627", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "from sklearn.datasets import load_iris\n", "from sklearn.tree import DecisionTreeClassifier\n", @@ -983,8 +874,10 @@ }, { "cell_type": "markdown", - "id": "e155d65a", - "metadata": {}, + "id": "1d734081", + "metadata": { + "editable": true + }, "source": [ "## Algorithms for Setting up Decision Trees\n", "\n", @@ -1001,8 +894,10 @@ }, { "cell_type": "markdown", - "id": "9f64d255", - "metadata": {}, + "id": "17e0e2f9", + "metadata": { + "editable": true + }, "source": [ "## The CART algorithm for Classification\n", "\n", @@ -1016,8 +911,10 @@ }, { "cell_type": "markdown", - "id": "c67ea6bd", - "metadata": {}, + "id": "73fc933b", + "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", @@ -1026,8 +923,10 @@ }, { "cell_type": "markdown", - "id": "60be0c2f", - "metadata": {}, + "id": "f8ca9923", + "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", @@ -1041,8 +940,10 @@ }, { "cell_type": "markdown", - "id": "68adc691", - "metadata": {}, + "id": "feb6fee3", + "metadata": { + "editable": true + }, "source": [ "## The CART algorithm for Regression\n", "\n", @@ -1052,8 +953,10 @@ }, { "cell_type": "markdown", - "id": "3aa84faa", - "metadata": {}, + "id": "24b0aea3", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}\\mathrm{MSE}_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}\\mathrm{MSE}_{\\mathrm{right}}.\n", @@ -1062,16 +965,20 @@ }, { "cell_type": "markdown", - "id": "05821fe6", - "metadata": {}, + "id": "8a01610a", + "metadata": { + "editable": true + }, "source": [ "Here the MSE for a specific node is defined as" ] }, { "cell_type": "markdown", - "id": "321fb878", - "metadata": {}, + "id": "9c28e4ab", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{MSE}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}(\\overline{y}_{\\mathrm{node}}-y_i)^2,\n", @@ -1080,16 +987,20 @@ }, { "cell_type": "markdown", - "id": "5703ea44", - "metadata": {}, + "id": "76dc3641", + "metadata": { + "editable": true + }, "source": [ "with" ] }, { "cell_type": "markdown", - "id": "6b6cf145", - "metadata": {}, + "id": "60f57c10", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\overline{y}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}y_i,\n", @@ -1098,8 +1009,10 @@ }, { "cell_type": "markdown", - "id": "57f159ee", - "metadata": {}, + "id": "8c3e3601", + "metadata": { + "editable": true + }, "source": [ "the mean value of all observations in a specific node.\n", "\n", @@ -1109,8 +1022,10 @@ }, { "cell_type": "markdown", - "id": "8dba6c9f", - "metadata": {}, + "id": "0be42f06", + "metadata": { + "editable": true + }, "source": [ "## Why binary splits?\n", "\n", @@ -1122,8 +1037,10 @@ }, { "cell_type": "markdown", - "id": "54686dd2", - "metadata": {}, + "id": "42ed666b", + "metadata": { + "editable": true + }, "source": [ "## Computing a Tree using the Gini Index\n", "\n", @@ -1146,8 +1063,10 @@ }, { "cell_type": "markdown", - "id": "226714bc", - "metadata": {}, + "id": "4ab2f2b9", + "metadata": { + "editable": true + }, "source": [ "## The Table\n", "\n", @@ -1172,8 +1091,10 @@ }, { "cell_type": "markdown", - "id": "132a6df7", - "metadata": {}, + "id": "df7b209e", + "metadata": { + "editable": true + }, "source": [ "## Computing the various Gini Indices\n", "\n", @@ -1187,8 +1108,10 @@ }, { "cell_type": "markdown", - "id": "75ab3e53", - "metadata": {}, + "id": "0f77f79d", + "metadata": { + "editable": true + }, "source": [ "## Computing the various Gini Indices, Hours slept\n", "\n", @@ -1199,8 +1122,10 @@ }, { "cell_type": "markdown", - "id": "be9d82ec", - "metadata": {}, + "id": "737f4e14", + "metadata": { + "editable": true + }, "source": [ "## Computing the various Gini Indices, Hours studied\n", "\n", @@ -1213,165 +1138,23 @@ }, { "cell_type": "markdown", - "id": "b502bb89", - "metadata": {}, + "id": "e2c139d1", + "metadata": { + "editable": true + }, "source": [ "## A possible code using Scikit-Learn" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "2e5fc857", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    " - ], - "text/plain": [ - " Grade Trend Hours slept Hours 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" - } - ], + "execution_count": 6, + "id": "d0089709", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "# Common imports\n", "import numpy as np\n", @@ -1437,8 +1220,10 @@ }, { "cell_type": "markdown", - "id": "fe2aa246", - "metadata": {}, + "id": "5f185aef", + "metadata": { + "editable": true + }, "source": [ "## Further example: Computing the Gini index\n", "\n", @@ -1479,8 +1264,10 @@ }, { "cell_type": "markdown", - "id": "46f289da", - "metadata": {}, + "id": "fe73a991", + "metadata": { + "editable": true + }, "source": [ "## Simple Python Code to read in Data and perform Classification" ] @@ -1488,8 +1275,11 @@ { "cell_type": "code", "execution_count": 7, - "id": "38aedbca", - "metadata": {}, + "id": "9b082c47", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# Common imports\n", @@ -1563,8 +1353,10 @@ }, { "cell_type": "markdown", - "id": "a6f5da59", - "metadata": {}, + "id": "df9287bb", + "metadata": { + "editable": true + }, "source": [ "## Computing the Gini Factor\n", "\n", @@ -1579,8 +1371,11 @@ { "cell_type": "code", "execution_count": 8, - "id": "e51855f9", - "metadata": {}, + "id": "00d95a16", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# Split a dataset based on an attribute and an attribute value\n", @@ -1647,8 +1442,10 @@ }, { "cell_type": "markdown", - "id": "f6add3e5", - "metadata": {}, + "id": "b6452b51", + "metadata": { + "editable": true + }, "source": [ "## Regression trees" ] @@ -1656,8 +1453,11 @@ { "cell_type": "code", "execution_count": 9, - "id": "74ecc649", - "metadata": {}, + "id": "3a98b310", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# Quadratic training set + noise\n", @@ -1671,8 +1471,11 @@ { "cell_type": "code", "execution_count": 10, - "id": "04024d89", - "metadata": {}, + "id": "1f8e183f", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", @@ -1683,8 +1486,10 @@ }, { "cell_type": "markdown", - "id": "878b4d23", - "metadata": {}, + "id": "6c91981e", + "metadata": { + "editable": true + }, "source": [ "## Final regressor code" ] @@ -1692,8 +1497,11 @@ { "cell_type": "code", "execution_count": 11, - "id": "3c96bff5", - "metadata": {}, + "id": "c9b69f54", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", @@ -1739,8 +1547,11 @@ { "cell_type": "code", "execution_count": 12, - "id": "527b27ca", - "metadata": {}, + "id": "53db8f73", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "tree_reg1 = DecisionTreeRegressor(random_state=42)\n", @@ -1775,8 +1586,10 @@ }, { "cell_type": "markdown", - "id": "f2a0dd48", - "metadata": {}, + "id": "3be38ddf", + "metadata": { + "editable": true + }, "source": [ "## Pros and cons of trees, pros\n", "\n", @@ -1797,8 +1610,10 @@ }, { "cell_type": "markdown", - "id": "9f896560", - "metadata": {}, + "id": "e4aaab5f", + "metadata": { + "editable": true + }, "source": [ "## Disadvantages\n", "\n", @@ -1823,8 +1638,10 @@ }, { "cell_type": "markdown", - "id": "3f6f50e2", - "metadata": {}, + "id": "0010cb58", + "metadata": { + "editable": true + }, "source": [ "## Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods\n", "\n", @@ -1852,8 +1669,10 @@ }, { "cell_type": "markdown", - "id": "24509012", - "metadata": {}, + "id": "f3a71c11", + "metadata": { + "editable": true + }, "source": [ "## An Overview of Ensemble Methods\n", "\n", @@ -1866,8 +1685,10 @@ }, { "cell_type": "markdown", - "id": "15a871bc", - "metadata": {}, + "id": "aef5e772", + "metadata": { + "editable": true + }, "source": [ "## Why Voting?\n", "\n", @@ -1888,8 +1709,10 @@ }, { "cell_type": "markdown", - "id": "e6d75533", - "metadata": {}, + "id": "a30b02f8", + "metadata": { + "editable": true + }, "source": [ "## Tossing coins\n", "\n", @@ -1917,8 +1740,10 @@ }, { "cell_type": "markdown", - "id": "8ecb23d0", - "metadata": {}, + "id": "3afd02ed", + "metadata": { + "editable": true + }, "source": [ "## Standard imports first" ] @@ -1926,8 +1751,11 @@ { "cell_type": "code", "execution_count": 13, - "id": "b42d0a08", - "metadata": {}, + "id": "4a1b7a89", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# Common imports\n", @@ -1971,8 +1799,10 @@ }, { "cell_type": "markdown", - "id": "e3060cfd", - "metadata": {}, + "id": "0c32e96d", + "metadata": { + "editable": true + }, "source": [ "## Simple Voting Example, head or tail" ] @@ -1980,8 +1810,11 @@ { "cell_type": "code", "execution_count": 14, - "id": "59d25264", - "metadata": {}, + "id": "4d1c99f7", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "\n", @@ -2011,8 +1844,10 @@ }, { "cell_type": "markdown", - "id": "f8cf0e6e", - "metadata": {}, + "id": "e0e3f1dc", + "metadata": { + "editable": true + }, "source": [ "## Using the Voting Classifier\n", "\n", @@ -2022,8 +1857,11 @@ { "cell_type": "code", "execution_count": 15, - "id": "76fd4c2d", - "metadata": {}, + "id": "c3d00f7d", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", @@ -2072,8 +1910,10 @@ }, { "cell_type": "markdown", - "id": "eacefe6c", - "metadata": {}, + "id": "f0d12f2c", + "metadata": { + "editable": true + }, "source": [ "## Voting and Bagging" ] @@ -2081,8 +1921,11 @@ { "cell_type": "code", "execution_count": 16, - "id": "796dfa6b", - "metadata": {}, + "id": "1bbde2a9", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", @@ -2108,8 +1951,11 @@ { "cell_type": "code", "execution_count": 17, - "id": "90ec162f", - "metadata": {}, + "id": "80a82744", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", @@ -2123,8 +1969,11 @@ { "cell_type": "code", "execution_count": 18, - "id": "e46dcb77", - "metadata": {}, + "id": "65c732b4", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "log_clf = LogisticRegression(random_state=42)\n", @@ -2140,8 +1989,11 @@ { "cell_type": "code", "execution_count": 19, - "id": "67a3b080", - "metadata": {}, + "id": "956f7ec5", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", @@ -2154,8 +2006,10 @@ }, { "cell_type": "markdown", - "id": "f0d51672", - "metadata": {}, + "id": "ee527167", + "metadata": { + "editable": true + }, "source": [ "## Bagging\n", "\n", @@ -2174,8 +2028,10 @@ }, { "cell_type": "markdown", - "id": "ab182ea8", - "metadata": {}, + "id": "354baed9", + "metadata": { + "editable": true + }, "source": [ "## More bagging\n", "\n", @@ -2204,8 +2060,10 @@ }, { "cell_type": "markdown", - "id": "998512be", - "metadata": {}, + "id": "fc1a2451", + "metadata": { + "editable": true + }, "source": [ "## Making your own Bootstrap: Changing the Level of the Decision Tree\n", "\n", @@ -2216,8 +2074,11 @@ { "cell_type": "code", "execution_count": 20, - "id": "6ac20f8b", - "metadata": {}, + "id": "129bb9fb", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "\n", @@ -2282,8 +2143,10 @@ }, { "cell_type": "markdown", - "id": "c9a44ff4", - "metadata": {}, + "id": "170b00ab", + "metadata": { + "editable": true + }, "source": [ "## Random forests\n", "\n", @@ -2303,8 +2166,10 @@ }, { "cell_type": "markdown", - "id": "74f9056f", - "metadata": {}, + "id": "58a4aa65", + "metadata": { + "editable": true + }, "source": [ "$$\n", "m\\approx \\sqrt{p}.\n", @@ -2313,8 +2178,10 @@ }, { "cell_type": "markdown", - "id": "9a0166e1", - "metadata": {}, + "id": "a5ed07c1", + "metadata": { + "editable": true + }, "source": [ "In building a random forest, at\n", "each split in the tree, the algorithm is not even allowed to consider\n", @@ -2336,8 +2203,10 @@ }, { "cell_type": "markdown", - "id": "7e5dd3c9", - "metadata": {}, + "id": "c5369edb", + "metadata": { + "editable": true + }, "source": [ "## Random Forest Algorithm\n", "The algorithm described here can be applied to both classification and regression problems.\n", @@ -2360,8 +2229,10 @@ }, { "cell_type": "markdown", - "id": "b2476d94", - "metadata": {}, + "id": "4355c5ad", + "metadata": { + "editable": true + }, "source": [ "## Random Forests Compared with other Methods on the Cancer Data" ] @@ -2369,8 +2240,11 @@ { "cell_type": "code", "execution_count": 21, - "id": "0a56d5de", - "metadata": {}, + "id": "0fbcd18f", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -2438,8 +2312,10 @@ }, { "cell_type": "markdown", - "id": "ef32420e", - "metadata": {}, + "id": "4072de62", + "metadata": { + "editable": true + }, "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", @@ -2452,8 +2328,10 @@ }, { "cell_type": "markdown", - "id": "5820ebfd", - "metadata": {}, + "id": "481673fb", + "metadata": { + "editable": true + }, "source": [ "## Compare Bagging on Trees with Random Forests" ] @@ -2461,8 +2339,11 @@ { "cell_type": "code", "execution_count": 22, - "id": "bb5bea62", - "metadata": {}, + "id": "d5fe5939", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "bag_clf = BaggingClassifier(\n", @@ -2473,8 +2354,11 @@ { "cell_type": "code", "execution_count": 23, - "id": "b879f3ce", - "metadata": {}, + "id": "7ee6160d", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "bag_clf.fit(X_train, y_train)\n", @@ -2488,8 +2372,10 @@ }, { "cell_type": "markdown", - "id": "60160f97", - "metadata": {}, + "id": "3a6e484c", + "metadata": { + "editable": true + }, "source": [ "## Boosting, a Bird's Eye View\n", "\n", @@ -2506,8 +2392,10 @@ }, { "cell_type": "markdown", - "id": "b351b1bd", - "metadata": {}, + "id": "ed91ea28", + "metadata": { + "editable": true + }, "source": [ "## What is boosting? Additive Modelling/Iterative Fitting\n", "\n", @@ -2518,8 +2406,10 @@ }, { "cell_type": "markdown", - "id": "6e9174ef", - "metadata": {}, + "id": "9ab6f212", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n", @@ -2528,8 +2418,10 @@ }, { "cell_type": "markdown", - "id": "fc319721", - "metadata": {}, + "id": "288a57f1", + "metadata": { + "editable": true + }, "source": [ "where $\\beta_m$ are the expansion parameters to be determined in a\n", "minimization process and $b(x;\\gamma_m)$ are some simple functions of\n", @@ -2543,8 +2435,10 @@ }, { "cell_type": "markdown", - "id": "da4ba861", - "metadata": {}, + "id": "d0ea7a14", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\sigma(t) = \\frac{1}{1+\\exp{(-t)}},\n", @@ -2553,8 +2447,10 @@ }, { "cell_type": "markdown", - "id": "f444a5a4", - "metadata": {}, + "id": "faa1b446", + "metadata": { + "editable": true + }, "source": [ "where $t=\\gamma_0+\\gamma_1 x$ and the parameters $\\gamma_0$ and\n", "$\\gamma_1$ were determined by the Logistic Regression fitting\n", @@ -2565,8 +2461,10 @@ }, { "cell_type": "markdown", - "id": "8a4d8175", - "metadata": {}, + "id": "a20c6de5", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(\\boldsymbol{y},\\boldsymbol{f}) = \\frac{1}{n} \\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n", @@ -2575,8 +2473,10 @@ }, { "cell_type": "markdown", - "id": "de12bc14", - "metadata": {}, + "id": "17800601", + "metadata": { + "editable": true + }, "source": [ "In this case the function $f(x)$ was replaced by the design matrix\n", "$\\boldsymbol{X}$ and the unknown linear regression parameters $\\boldsymbol{\\beta}$,\n", @@ -2586,8 +2486,10 @@ }, { "cell_type": "markdown", - "id": "735bf417", - "metadata": {}, + "id": "7b59e224", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\boldsymbol{\\beta}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n", @@ -2596,16 +2498,20 @@ }, { "cell_type": "markdown", - "id": "22b8d82f", - "metadata": {}, + "id": "f5917a9a", + "metadata": { + "editable": true + }, "source": [ "In iterative fitting or additive modeling, we minimize the cost function with respect to the parameters $\\beta_m$ and $\\gamma_m$." ] }, { "cell_type": "markdown", - "id": "661db2e1", - "metadata": {}, + "id": "8d2101f2", + "metadata": { + "editable": true + }, "source": [ "## Iterative Fitting, Regression and Squared-error Cost Function\n", "\n", @@ -2630,8 +2536,10 @@ }, { "cell_type": "markdown", - "id": "2b14c81e", - "metadata": {}, + "id": "65f77895", + "metadata": { + "editable": true + }, "source": [ "## Squared-Error Example and Iterative Fitting\n", "\n", @@ -2644,8 +2552,10 @@ }, { "cell_type": "markdown", - "id": "64c44231", - "metadata": {}, + "id": "28b76851", + "metadata": { + "editable": true + }, "source": [ "$$\n", "(\\beta_m,\\gamma_m) = \\mathrm{argmin}_{\\beta,\\lambda}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta b(x;\\gamma))^2=\\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta(1+\\gamma x_i))^2.\n", @@ -2654,8 +2564,10 @@ }, { "cell_type": "markdown", - "id": "1040bdaf", - "metadata": {}, + "id": "985ccfb2", + "metadata": { + "editable": true + }, "source": [ "We start our iteration by simply setting $f_0(x)=0$. \n", "Taking the derivatives with respect to $\\beta$ and $\\gamma$ we obtain" @@ -2663,8 +2575,10 @@ }, { "cell_type": "markdown", - "id": "de59d269", - "metadata": {}, + "id": "05d75812", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial {\\cal C}}{\\partial \\beta} = -2\\sum_{i}(1+\\gamma x_i)(y_i-\\beta(1+\\gamma x_i))=0,\n", @@ -2673,16 +2587,20 @@ }, { "cell_type": "markdown", - "id": "5f87e844", - "metadata": {}, + "id": "9cfc5c74", + "metadata": { + "editable": true + }, "source": [ "and" ] }, { "cell_type": "markdown", - "id": "a2f9215c", - "metadata": {}, + "id": "3833eb7b", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial {\\cal C}}{\\partial \\gamma} =-2\\sum_{i}\\beta x_i(y_i-\\beta(1+\\gamma x_i))=0.\n", @@ -2691,16 +2609,20 @@ }, { "cell_type": "markdown", - "id": "67f71f90", - "metadata": {}, + "id": "3fca374c", + "metadata": { + "editable": true + }, "source": [ "We can then rewrite these equations as (defining $\\boldsymbol{w}=\\boldsymbol{e}+\\gamma \\boldsymbol{x})$ with $\\boldsymbol{e}$ being the unit vector)" ] }, { "cell_type": "markdown", - "id": "5410f260", - "metadata": {}, + "id": "aeb623d7", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\gamma \\boldsymbol{w}^T(\\boldsymbol{y}-\\beta\\gamma \\boldsymbol{w})=0,\n", @@ -2709,16 +2631,20 @@ }, { "cell_type": "markdown", - "id": "0485a1f5", - "metadata": {}, + "id": "f8e542a2", + "metadata": { + "editable": true + }, "source": [ "which gives us $\\beta = \\boldsymbol{w}^T\\boldsymbol{y}/(\\boldsymbol{w}^T\\boldsymbol{w})$. Similarly we have" ] }, { "cell_type": "markdown", - "id": "3a256711", - "metadata": {}, + "id": "d92e3136", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\beta\\gamma \\boldsymbol{x}^T(\\boldsymbol{y}-\\beta(1+\\gamma \\boldsymbol{x}))=0,\n", @@ -2727,8 +2653,10 @@ }, { "cell_type": "markdown", - "id": "fc8cd2ae", - "metadata": {}, + "id": "bca4d27a", + "metadata": { + "editable": true + }, "source": [ "which leads to $\\gamma =(\\boldsymbol{x}^T\\boldsymbol{y}-\\beta\\boldsymbol{x}^T\\boldsymbol{e})/(\\beta\\boldsymbol{x}^T\\boldsymbol{x})$. Inserting\n", "for $\\beta$ gives us an equation for $\\gamma$. This is a non-linear equation in the unknown $\\gamma$ and has to be solved numerically. \n", @@ -2739,8 +2667,10 @@ }, { "cell_type": "markdown", - "id": "0a9ecf4b", - "metadata": {}, + "id": "d2fa1316", + "metadata": { + "editable": true + }, "source": [ "## Iterative Fitting, Classification and AdaBoost\n", "\n", @@ -2753,8 +2683,10 @@ }, { "cell_type": "markdown", - "id": "ac605ae7", - "metadata": {}, + "id": "5401b686", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{\\overline{err}}=\\frac{1}{n} \\sum_{i=0}^{n-1} I(y_i\\ne G(x_i)).\n", @@ -2763,8 +2695,10 @@ }, { "cell_type": "markdown", - "id": "b4d530db", - "metadata": {}, + "id": "082464c4", + "metadata": { + "editable": true + }, "source": [ "The iterative procedure starts with defining a weak classifier whose\n", "error rate is barely better than random guessing. The iterative\n", @@ -2777,8 +2711,10 @@ }, { "cell_type": "markdown", - "id": "0f9fce0f", - "metadata": {}, + "id": "4f2a4a17", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n", @@ -2787,16 +2723,20 @@ }, { "cell_type": "markdown", - "id": "73471c17", - "metadata": {}, + "id": "4aad349a", + "metadata": { + "editable": true + }, "source": [ "will be a function of" ] }, { "cell_type": "markdown", - "id": "d8244842", - "metadata": {}, + "id": "d2050bb1", + "metadata": { + "editable": true + }, "source": [ "$$\n", "G_M(x) = \\mathrm{sign} \\sum_{i=1}^M \\alpha_m G_m(x).\n", @@ -2805,8 +2745,10 @@ }, { "cell_type": "markdown", - "id": "be09fe99", - "metadata": {}, + "id": "359b0eb3", + "metadata": { + "editable": true + }, "source": [ "## Adaptive Boosting, AdaBoost\n", "\n", @@ -2815,8 +2757,10 @@ }, { "cell_type": "markdown", - "id": "a547cf77", - "metadata": {}, + "id": "eab77ff9", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_m(x) = f_{m-1}(x)+\\beta_mG_m(x).\n", @@ -2825,8 +2769,10 @@ }, { "cell_type": "markdown", - "id": "67b1198a", - "metadata": {}, + "id": "d7c87ec5", + "metadata": { + "editable": true + }, "source": [ "The simplest possible cost function which leads (also simple from a computational point of view) to the AdaBoost algorithm is the\n", "exponential cost/loss function defined as" @@ -2834,8 +2780,10 @@ }, { "cell_type": "markdown", - "id": "f0a75e83", - "metadata": {}, + "id": "3cf53a5f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}\\exp{(-y_i(f_{m-1}(x_i)+\\beta G(x_i))}.\n", @@ -2844,8 +2792,10 @@ }, { "cell_type": "markdown", - "id": "9d2d96dc", - "metadata": {}, + "id": "67bbb3ac", + "metadata": { + "editable": true + }, "source": [ "We optimize $\\beta$ and $G$ for each value of $m=1:M$ as we did in the regression case.\n", "This is normally done in two steps. Let us however first rewrite the cost function as" @@ -2853,8 +2803,10 @@ }, { "cell_type": "markdown", - "id": "a6c2a558", - "metadata": {}, + "id": "d9a2448e", + "metadata": { + "editable": true + }, "source": [ "$$\n", "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}w_i^{m}\\exp{(-y_i\\beta G(x_i))},\n", @@ -2863,16 +2815,20 @@ }, { "cell_type": "markdown", - "id": "5a582df6", - "metadata": {}, + "id": "392f16f2", + "metadata": { + "editable": true + }, "source": [ "where we have defined $w_i^m= \\exp{(-y_if_{m-1}(x_i))}$." ] }, { "cell_type": "markdown", - "id": "654c5f13", - "metadata": {}, + "id": "a645e83c", + "metadata": { + "editable": true + }, "source": [ "## Building up AdaBoost\n", "\n", @@ -2881,8 +2837,10 @@ }, { "cell_type": "markdown", - "id": "efecb2bc", - "metadata": {}, + "id": "89e1d11e", + "metadata": { + "editable": true + }, "source": [ "$$\n", "G_m(x) = \\mathrm{sign} \\sum_{i=0}^{n-1} w_i^m I(y_i \\ne G_(x_i)),\n", @@ -2891,8 +2849,10 @@ }, { "cell_type": "markdown", - "id": "da78bb27", - "metadata": {}, + "id": "f884f217", + "metadata": { + "editable": true + }, "source": [ "which is the classifier that minimizes the weighted error rate in predicting $y$.\n", "\n", @@ -2901,8 +2861,10 @@ }, { "cell_type": "markdown", - "id": "dc1c118f", - "metadata": {}, + "id": "664acf19", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\exp{-(\\beta)}\\sum_{y_i=G(x_i)}w_i^m+\\exp{(\\beta)}\\sum_{y_i\\ne G(x_i)}w_i^m,\n", @@ -2911,16 +2873,20 @@ }, { "cell_type": "markdown", - "id": "1b77640d", - "metadata": {}, + "id": "d83ebe5e", + "metadata": { + "editable": true + }, "source": [ "which can be rewritten as" ] }, { "cell_type": "markdown", - "id": "d7944742", - "metadata": {}, + "id": "96134017", + "metadata": { + "editable": true + }, "source": [ "$$\n", "(\\exp{(\\beta)}-\\exp{-(\\beta)})\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i))+\\exp{(-\\beta)}\\sum_{i=0}^{n-1}w_i^m=0,\n", @@ -2929,16 +2895,20 @@ }, { "cell_type": "markdown", - "id": "94ffa0c4", - "metadata": {}, + "id": "97fc79c8", + "metadata": { + "editable": true + }, "source": [ "which leads to" ] }, { "cell_type": "markdown", - "id": "eae46622", - "metadata": {}, + "id": "0d1e4f3e", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\beta_m = \\frac{1}{2}\\log{\\frac{1-\\mathrm{\\overline{err}}}{\\mathrm{\\overline{err}}}},\n", @@ -2947,16 +2917,20 @@ }, { "cell_type": "markdown", - "id": "099f71b5", - "metadata": {}, + "id": "7d571a24", + "metadata": { + "editable": true + }, "source": [ "where we have redefined the error as" ] }, { "cell_type": "markdown", - "id": "11e5f200", - "metadata": {}, + "id": "8f31a3a7", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{\\overline{err}}_m=\\frac{1}{n}\\frac{\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i)}{\\sum_{i=0}^{n-1}w_i^m},\n", @@ -2965,16 +2939,20 @@ }, { "cell_type": "markdown", - "id": "77b52ed2", - "metadata": {}, + "id": "e0e3db77", + "metadata": { + "editable": true + }, "source": [ "which leads to an update of" ] }, { "cell_type": "markdown", - "id": "e8fe5df6", - "metadata": {}, + "id": "fc1ce185", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f_m(x) = f_{m-1}(x) +\\beta_m G_m(x).\n", @@ -2983,16 +2961,20 @@ }, { "cell_type": "markdown", - "id": "4c1ea9b7", - "metadata": {}, + "id": "d7360d72", + "metadata": { + "editable": true + }, "source": [ "This leads to the new weights" ] }, { "cell_type": "markdown", - "id": "a61b875a", - "metadata": {}, + "id": "cf486320", + "metadata": { + "editable": true + }, "source": [ "$$\n", "w_i^{m+1} = w_i^m \\exp{(-y_i\\beta_m G_m(x_i))}\n", @@ -3001,8 +2983,10 @@ }, { "cell_type": "markdown", - "id": "a0df6e36", - "metadata": {}, + "id": "8ebdf8ce", + "metadata": { + "editable": true + }, "source": [ "## Adaptive boosting: AdaBoost, Basic Algorithm\n", "\n", @@ -3019,8 +3003,10 @@ }, { "cell_type": "markdown", - "id": "862806de", - "metadata": {}, + "id": "853a1218", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{err}=\\frac{1}{n}\\sum_{i=0}^{n-1}I(y_i\\ne G(x_i)),\n", @@ -3029,16 +3015,20 @@ }, { "cell_type": "markdown", - "id": "60c6b96e", - "metadata": {}, + "id": "d287b19d", + "metadata": { + "editable": true + }, "source": [ "where the function $I()$ is one if we misclassify and zero if we classify correctly." ] }, { "cell_type": "markdown", - "id": "d4cf16bb", - "metadata": {}, + "id": "637ef064", + "metadata": { + "editable": true + }, "source": [ "## Basic Steps of AdaBoost\n", "\n", @@ -3051,8 +3041,10 @@ }, { "cell_type": "markdown", - "id": "91e907b9", - "metadata": {}, + "id": "f1698dc3", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathrm{\\overline{err}}_m=\\frac{\\sum_{i=0}^{n-1}w_i^m I(y_i\\ne G(x_i))}{\\sum_{i=0}^{n-1}w_i},\n", @@ -3061,8 +3053,10 @@ }, { "cell_type": "markdown", - "id": "cc913a38", - "metadata": {}, + "id": "c6d08c01", + "metadata": { + "editable": true + }, "source": [ "1. Then we start looping over all attempts at classifying, namely we start an iterative process for $m=1:M$, where $M$ is the final number of classifications. Our given classifier could for example be a plain decision tree.\n", "\n", @@ -3087,8 +3081,10 @@ }, { "cell_type": "markdown", - "id": "87e49535", - "metadata": {}, + "id": "c58db919", + "metadata": { + "editable": true + }, "source": [ "## AdaBoost Examples\n", "\n", @@ -3098,8 +3094,11 @@ { "cell_type": "code", "execution_count": 24, - "id": "a48ac6a2", - "metadata": {}, + "id": "104e1c7c", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.ensemble import AdaBoostClassifier\n", @@ -3126,25 +3125,7 @@ ] } ], - "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": "3.9.10" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }