FICOexplainability.ipynb

ficoexplainability.ipynb

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  "metadata": {
    "colab": {
      "name": "FICOexplainability.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/gist/gjlr2000/ddb1b81c42f136fa235316103313408d/ficoexplainability.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "JmM7FayxP-1E",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# FICO explainability contest\n",
        "\n",
        "Fair Isaac Company ([FICO](https://en.wikipedia.org/wiki/FICO)) created a machine learning contest: \n",
        "\n",
        "\n",
        "> *[The Explainable Machine Learning Challenge](https://community.fico.com/community/xml) is a collaboration between Google, FICO and academics at Berkeley, Oxford, Imperial, UC Irvine and MIT, to generate new research in the area of algorithmic explainability. Teams will be challenged to create machine learning models with both high accuracy and explainability; they will use a real-world financial dataset provided by FICO*\n",
        "\n",
        "The motivation is simple: regulation.\n",
        "\n",
        "> *In settings where **regulators** or consumers demand explanations, understanding the structure and predictions of these models will pave the way for their wide adoption in practice.*\n",
        "\n",
        "I explained the mechanics of [AI as a White Box](https://medium.com/@gjlr2000/ai-as-a-white-box-in-finance-e6f3216aa7e7) in  previous blog, and I can show you how we can very quickly cannibalize the previous example (credit card fraud) to develop a white box AI system to decide credit applications. \n",
        "\n",
        "Decision Trees and Random Forests (of Decision Trees) already have been 'explained', but I will be looking closely at the results of the challege, as the goal is to develop further 'explainability' models for techniques which still lack them.\n",
        "\n",
        "Meanwhile, lets load the contest data:\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "vwEjZGFc2KnW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "70b1e23a-520e-48a2-aea1-57751204b493"
      },
      "cell_type": "code",
      "source": [
        "# Downloading required data and previous examples \n",
        "#\n",
        "# Steps to download big files from a shared good drive: https://gist.github.com/iamtekeste/3cdfd0366ebfd2c0d805\n",
        "# I kept the Credit Card data available in a public google drive \n",
        "# (so that you do not need to download it on your own, not open an account at Kaggle)\n",
        "\n",
        "#!wget -o /dev/null --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1O0E0Re1nA34BBPBr_TQfUE_IOHGkkeUR' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1O0E0Re1nA34BBPBr_TQfUE_IOHGkkeUR\" -O creditcard.csv && rm -rf /tmp/cookies.txt  \n",
        "#https://drive.google.com/file/d/1Omau2jW9UWDIPBvNsffNQk4Dz5t4gta9/view?usp=sharing\n",
        "!wget -o /dev/null --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1Omau2jW9UWDIPBvNsffNQk4Dz5t4gta9' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1Omau2jW9UWDIPBvNsffNQk4Dz5t4gta9\" -O contest.csv && rm -rf /tmp/cookies.txt  \n",
        "#https://drive.google.com/file/d/1_WXIFg21hUgTQzFUmStAoZVzdKd35zVV/view?usp=sharing\n",
        "!wget -o /dev/null --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1_WXIFg21hUgTQzFUmStAoZVzdKd35zVV' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1_WXIFg21hUgTQzFUmStAoZVzdKd35zVV\" -O helocDecisionTree.jpg && rm -rf /tmp/cookies.txt  \n",
        "#!wget -o /dev/null --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=14znRMmOwPfV4jEmFK7M7vWKDGNL4sdoJ' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=14znRMmOwPfV4jEmFK7M7vWKDGNL4sdoJ\" -O CreditCardFullDecisionTree.jpg && rm -rf /tmp/cookies.txt \n",
        "!ls\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "contest.csv\t\t    CreditCardFullDecisionTree.jpg  outD3.dot\r\n",
            "creditcard.csv\t\t    datalab\t\t\t    out.dot\r\n",
            "CreditCardDecisionTree.jpg  helocDecisionTree.jpg\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "auwf8tUbVECe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The following are the machine learning packages already available in python."
      ]
    },
    {
      "metadata": {
        "id": "QazGBHO91Zgt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "565cb0f1-20a4-47c0-90e0-faaee7a05f3f"
      },
      "cell_type": "code",
      "source": [
        "# -*- coding: utf-8 -*-\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import SGDClassifier, PassiveAggressiveClassifier, Perceptron\n",
        "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "import warnings\n",
        "!pip install tqdm\n",
        "from tqdm import *\n",
        "\n",
        "warnings.filterwarnings(\"ignore\",category=DeprecationWarning)\n",
        "data_org=pd.read_csv('contest.csv')\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (4.23.4)\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zCkBQmdSVC-5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The variable 'data' has all the anonymized credit applications. The first column (RiskPerformance) identifies whether an  application turned out to be 'Bad' or 'Good' (whether the loan was not paid in time or not).\n",
        "\n",
        "The description are quite good:\n",
        "\n",
        "```\n",
        "     RiskPerformance\t            Paid as negotiated flag (12-36 Months). \n",
        "                                    String of Good and Bad\n",
        "[0]  ExternalRiskEstimate\t       Consolidated version of risk markers\n",
        "[1]  MSinceOldestTradeOpen\t      Months Since Oldest Trade Open\n",
        "[2]  MSinceMostRecentTradeOpen\t  Months Since Most Recent Trade Open\n",
        "[3]  AverageMInFile\t             Average Months in File\n",
        "[4]  NumSatisfactoryTrades\t      Number Satisfactory Trades\n",
        "[5]  NumTrades60Ever2DerogPubRec\tNumber Trades 60+ Ever\n",
        "[6]  NumTrades90Ever2DerogPubRec\tNumber Trades 90+ Ever\n",
        "[7]  PercentTradesNeverDelq\t     Percent Trades Never Delinquent\n",
        "[8]  MSinceMostRecentDelq\t       Months Since Most Recent Delinquency\n",
        "[9]  MaxDelq2PublicRecLast12M\t   Max Delq/Public Records Last 12 Months. \n",
        "                                    See tab \"MaxDelq\" for each category\n",
        "[10] MaxDelqEver\t                Max Delinquency Ever. \n",
        "                                    See tab \"MaxDelq\" for each category\n",
        "[11] NumTotalTrades\t             Number of Total Trades (total number of credit accounts)\n",
        "[12] NumTradesOpeninLast12M\t     Number of Trades Open in Last 12 Months\n",
        "[13] PercentInstallTrades\t       Percent Installment Trades\n",
        "[14] MSinceMostRecentInqexcl7days   Months Since Most Recent Inq excl 7days\n",
        "[15] NumInqLast6M\t               Number of Inq Last 6 Months\n",
        "[16] NumInqLast6Mexcl7days\t      Number of Inq Last 6 Months excl 7days. \n",
        "                                    Excluding the last 7 days removes inquiries that are likely due to \n",
        "                                    price comparision shopping.\n",
        "[17] NetFractionRevolvingBurden\t Net Fraction Revolving Burden. \n",
        "                                    This is revolving balance divided by credit limit\n",
        "[18] NetFractionInstallBurden\t   Net Fraction Installment Burden. \n",
        "                                    This is installment balance divided by original loan amount\n",
        "[19] NumRevolvingTradesWBalance\t Number Revolving Trades with Balance\n",
        "[20] NumInstallTradesWBalance\t   Number Installment Trades with Balance\n",
        "[21] NumBank2NatlTradesWHighUtilization  Number Bank/Natl Trades w high utilization ratio\n",
        "[22] PercentTradesWBalance\t      Percent Trades with Balance\n",
        "```\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "YqmjYXlJVA6R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "392dd34c-61bf-47ce-8df5-ec3ef5f68463"
      },
      "cell_type": "code",
      "source": [
        "data_org.head()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RiskPerformance</th>\n",
              "      <th>ExternalRiskEstimate</th>\n",
              "      <th>MSinceOldestTradeOpen</th>\n",
              "      <th>MSinceMostRecentTradeOpen</th>\n",
              "      <th>AverageMInFile</th>\n",
              "      <th>NumSatisfactoryTrades</th>\n",
              "      <th>NumTrades60Ever2DerogPubRec</th>\n",
              "      <th>NumTrades90Ever2DerogPubRec</th>\n",
              "      <th>PercentTradesNeverDelq</th>\n",
              "      <th>MSinceMostRecentDelq</th>\n",
              "      <th>...</th>\n",
              "      <th>PercentInstallTrades</th>\n",
              "      <th>MSinceMostRecentInqexcl7days</th>\n",
              "      <th>NumInqLast6M</th>\n",
              "      <th>NumInqLast6Mexcl7days</th>\n",
              "      <th>NetFractionRevolvingBurden</th>\n",
              "      <th>NetFractionInstallBurden</th>\n",
              "      <th>NumRevolvingTradesWBalance</th>\n",
              "      <th>NumInstallTradesWBalance</th>\n",
              "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
              "      <th>PercentTradesWBalance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bad</td>\n",
              "      <td>55</td>\n",
              "      <td>144</td>\n",
              "      <td>4</td>\n",
              "      <td>84</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>83</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>-8</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bad</td>\n",
              "      <td>61</td>\n",
              "      <td>58</td>\n",
              "      <td>15</td>\n",
              "      <td>41</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>100</td>\n",
              "      <td>-7</td>\n",
              "      <td>...</td>\n",
              "      <td>67</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-8</td>\n",
              "      <td>0</td>\n",
              "      <td>-8</td>\n",
              "      <td>-8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bad</td>\n",
              "      <td>67</td>\n",
              "      <td>66</td>\n",
              "      <td>5</td>\n",
              "      <td>24</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>-7</td>\n",
              "      <td>...</td>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>53</td>\n",
              "      <td>66</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bad</td>\n",
              "      <td>66</td>\n",
              "      <td>169</td>\n",
              "      <td>1</td>\n",
              "      <td>73</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>76</td>\n",
              "      <td>...</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>72</td>\n",
              "      <td>83</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bad</td>\n",
              "      <td>81</td>\n",
              "      <td>333</td>\n",
              "      <td>27</td>\n",
              "      <td>132</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>-7</td>\n",
              "      <td>...</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>51</td>\n",
              "      <td>89</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  RiskPerformance  ExternalRiskEstimate  MSinceOldestTradeOpen  \\\n",
              "0             Bad                    55                    144   \n",
              "1             Bad                    61                     58   \n",
              "2             Bad                    67                     66   \n",
              "3             Bad                    66                    169   \n",
              "4             Bad                    81                    333   \n",
              "\n",
              "   MSinceMostRecentTradeOpen  AverageMInFile  NumSatisfactoryTrades  \\\n",
              "0                          4              84                     20   \n",
              "1                         15              41                      2   \n",
              "2                          5              24                      9   \n",
              "3                          1              73                     28   \n",
              "4                         27             132                     12   \n",
              "\n",
              "   NumTrades60Ever2DerogPubRec  NumTrades90Ever2DerogPubRec  \\\n",
              "0                            3                            0   \n",
              "1                            4                            4   \n",
              "2                            0                            0   \n",
              "3                            1                            1   \n",
              "4                            0                            0   \n",
              "\n",
              "   PercentTradesNeverDelq  MSinceMostRecentDelq          ...            \\\n",
              "0                      83                     2          ...             \n",
              "1                     100                    -7          ...             \n",
              "2                     100                    -7          ...             \n",
              "3                      93                    76          ...             \n",
              "4                     100                    -7          ...             \n",
              "\n",
              "   PercentInstallTrades  MSinceMostRecentInqexcl7days  NumInqLast6M  \\\n",
              "0                    43                             0             0   \n",
              "1                    67                             0             0   \n",
              "2                    44                             0             4   \n",
              "3                    57                             0             5   \n",
              "4                    25                             0             1   \n",
              "\n",
              "   NumInqLast6Mexcl7days  NetFractionRevolvingBurden  \\\n",
              "0                      0                          33   \n",
              "1                      0                           0   \n",
              "2                      4                          53   \n",
              "3                      4                          72   \n",
              "4                      1                          51   \n",
              "\n",
              "   NetFractionInstallBurden  NumRevolvingTradesWBalance  \\\n",
              "0                        -8                           8   \n",
              "1                        -8                           0   \n",
              "2                        66                           4   \n",
              "3                        83                           6   \n",
              "4                        89                           3   \n",
              "\n",
              "   NumInstallTradesWBalance  NumBank2NatlTradesWHighUtilization  \\\n",
              "0                         1                                   1   \n",
              "1                        -8                                  -8   \n",
              "2                         2                                   1   \n",
              "3                         4                                   3   \n",
              "4                         1                                   0   \n",
              "\n",
              "   PercentTradesWBalance  \n",
              "0                     69  \n",
              "1                      0  \n",
              "2                     86  \n",
              "3                     91  \n",
              "4                     80  \n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "metadata": {
        "id": "qOo7aYx-Wyjq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "I already fitted a simple decision tree with 3 branches to show how a classification can work (and be understood by a human) - see the image below.\n",
        "\n",
        "The variable X[0] corresponds to ExternalRiskEstimate, X[3]  AverageMInFile, X[4] NumSatisfactoryTrades, X[7] PercentTradesNeverDelq, X[22] PercentTradesWBalance.\n",
        "\n",
        "The last row has the branches with classified examples - we want to make sure the last branch has either a low 'gini' number. Look at the second from the left: it has a gini of 0.326, with [334, 1295] which correspond to 334 Good credits versus 1295 Bad ones.\n",
        "\n",
        "To get there we had to:\n",
        "1. start at the top X[0] ExternalRiskEstimate, if is less or equal to 74.5 check if\n",
        "2.  X[0] ExternalRiskEstimate is less or equal than 67.5, if so then\n",
        "3. check if X[22] PercentTradesWBalance is greater than 48\n",
        "\n",
        "Most of credits that fall in that bucket (1295/1629 = 79.5%) are bad. We could keep adding further decision points but that muddles the understanding.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "B9YXaBmlO6kU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "b50c62bd-9f35-4907-ed31-7581fe19c751"
      },
      "cell_type": "code",
      "source": [
        "from IPython.display import Image\n",
        "Image('helocDecisionTree.jpg', width = 900) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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CbL9AWb+2fv36Eau1Kw2w7fAiymDBBB1BbarBWWedNaLc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\nAdj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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/jpeg": {
              "width": 900
            }
          },
          "execution_count": 135
        }
      ]
    },
    {
      "metadata": {
        "id": "gCOqubf_IJy8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can also see an example of 'Good' credits: the last box on the last row has a gini of 0.31, with 1054 good and 250 bad credits. To get there,\n",
        "\n",
        "\n",
        "1.   X[0] ExternalRiskEstimate has to be greater than 74.5, if so then\n",
        "2.   X[3] Average Months in File has to be greater than 65.5, if so then\n",
        "3. X[7] Percent Trades Never Delinquent has to be greater than 95.5; Only 250/1304 = 19% of customers in this category were bad credits.\n",
        "\n",
        "\n",
        "\n",
        "The goal of the contest is to explain to a customer/regulator why he was rejected/approved. The next step would be to create a template that can produce the above list automatically from the decision tree, adding a line explaining that after following this branch the bank decided that as X% of customers in this category were bad credits, the credit was approved/rejected.\n",
        "\n",
        "Regulator and Customers want to have an explanation, as borderline cases could then have the option of changing their behaviuour to qualify to a loan (if they were rejected)\n",
        "\n",
        "**Note** It seems like the most important predictor is this 'External Risk Estimate'. Frankly, it seems as this example all it does is to shift the blame from this company to another (the 'External Risk Estimate' provider). How do they estimate this parameter is not explained anywhere, hence 'explainability' is moot.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "708REmRYZuEp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "You wil notice that we have a column called 'RiskPerformance' which tells us the kind (Good or Bad) of an application (with hindsight).\n",
        "\n",
        "Because we have that 'labeled' data, we use a **supervised** machine learning method. When we do not have the labels available, we use **unsupervised** methods -- see ['Intelligence Amplification in Finance'](https://www.linkedin.com/pulse/intelligence-amplification-finance-gerardo-lemus/) where a clustering method is used to identify different regimes.\n",
        "\n",
        "Below we will 'train different methods (among them, single Decision Tree and 100 trees Random Forest) on our data, making sure we split the data in two sets, one for training and another for testing. \n",
        "\n",
        "My goal is to show how a trained model can be used to explain decision (not to pick up which one is best). If I were looking to choose the 'best' AI model, I would need to make sure I am not ['p-hacking'](https://s3.amazonaws.com/com.ravenpack.cms/pages/RavenPack_Machine_Learning_-_next_Steps_foir_Finance__Mark_Salmon_Cambridge_University25C53EFB.pdf) (or throwing so many models to the *same* data set that one looks very good just by chance - see this [Xkcd cartoon](http://xkcdsucks.blogspot.com/2011/04/comic-882-signifying-nothing.html) for a humorous explanation).  \n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "EmV3aK2gl3SR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "c932fce2-c00f-47f1-90a6-5785cf993f11"
      },
      "cell_type": "code",
      "source": [
        "target_class=data_org[['RiskPerformance']]\n",
        "data = data_org.drop('RiskPerformance',axis=1)\n",
        "data.head()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ExternalRiskEstimate</th>\n",
              "      <th>MSinceOldestTradeOpen</th>\n",
              "      <th>MSinceMostRecentTradeOpen</th>\n",
              "      <th>AverageMInFile</th>\n",
              "      <th>NumSatisfactoryTrades</th>\n",
              "      <th>NumTrades60Ever2DerogPubRec</th>\n",
              "      <th>NumTrades90Ever2DerogPubRec</th>\n",
              "      <th>PercentTradesNeverDelq</th>\n",
              "      <th>MSinceMostRecentDelq</th>\n",
              "      <th>MaxDelq2PublicRecLast12M</th>\n",
              "      <th>...</th>\n",
              "      <th>PercentInstallTrades</th>\n",
              "      <th>MSinceMostRecentInqexcl7days</th>\n",
              "      <th>NumInqLast6M</th>\n",
              "      <th>NumInqLast6Mexcl7days</th>\n",
              "      <th>NetFractionRevolvingBurden</th>\n",
              "      <th>NetFractionInstallBurden</th>\n",
              "      <th>NumRevolvingTradesWBalance</th>\n",
              "      <th>NumInstallTradesWBalance</th>\n",
              "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
              "      <th>PercentTradesWBalance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>55</td>\n",
              "      <td>144</td>\n",
              "      <td>4</td>\n",
              "      <td>84</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>83</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>-8</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>69</td>\n",
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              "      <td>61</td>\n",
              "      <td>58</td>\n",
              "      <td>15</td>\n",
              "      <td>41</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>100</td>\n",
              "      <td>-7</td>\n",
              "      <td>0</td>\n",
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              "      <td>67</td>\n",
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              "      <td>67</td>\n",
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              "      <td>24</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>-7</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>53</td>\n",
              "      <td>66</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>66</td>\n",
              "      <td>169</td>\n",
              "      <td>1</td>\n",
              "      <td>73</td>\n",
              "      <td>28</td>\n",
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              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>76</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>72</td>\n",
              "      <td>83</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>81</td>\n",
              "      <td>333</td>\n",
              "      <td>27</td>\n",
              "      <td>132</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>-7</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>51</td>\n",
              "      <td>89</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   ExternalRiskEstimate  MSinceOldestTradeOpen  MSinceMostRecentTradeOpen  \\\n",
              "0                    55                    144                          4   \n",
              "1                    61                     58                         15   \n",
              "2                    67                     66                          5   \n",
              "3                    66                    169                          1   \n",
              "4                    81                    333                         27   \n",
              "\n",
              "   AverageMInFile  NumSatisfactoryTrades  NumTrades60Ever2DerogPubRec  \\\n",
              "0              84                     20                            3   \n",
              "1              41                      2                            4   \n",
              "2              24                      9                            0   \n",
              "3              73                     28                            1   \n",
              "4             132                     12                            0   \n",
              "\n",
              "   NumTrades90Ever2DerogPubRec  PercentTradesNeverDelq  MSinceMostRecentDelq  \\\n",
              "0                            0                      83                     2   \n",
              "1                            4                     100                    -7   \n",
              "2                            0                     100                    -7   \n",
              "3                            1                      93                    76   \n",
              "4                            0                     100                    -7   \n",
              "\n",
              "   MaxDelq2PublicRecLast12M          ...            PercentInstallTrades  \\\n",
              "0                         3          ...                              43   \n",
              "1                         0          ...                              67   \n",
              "2                         7          ...                              44   \n",
              "3                         6          ...                              57   \n",
              "4                         7          ...                              25   \n",
              "\n",
              "   MSinceMostRecentInqexcl7days  NumInqLast6M  NumInqLast6Mexcl7days  \\\n",
              "0                             0             0                      0   \n",
              "1                             0             0                      0   \n",
              "2                             0             4                      4   \n",
              "3                             0             5                      4   \n",
              "4                             0             1                      1   \n",
              "\n",
              "   NetFractionRevolvingBurden  NetFractionInstallBurden  \\\n",
              "0                          33                        -8   \n",
              "1                           0                        -8   \n",
              "2                          53                        66   \n",
              "3                          72                        83   \n",
              "4                          51                        89   \n",
              "\n",
              "   NumRevolvingTradesWBalance  NumInstallTradesWBalance  \\\n",
              "0                           8                         1   \n",
              "1                           0                        -8   \n",
              "2                           4                         2   \n",
              "3                           6                         4   \n",
              "4                           3                         1   \n",
              "\n",
              "   NumBank2NatlTradesWHighUtilization  PercentTradesWBalance  \n",
              "0                                   1                     69  \n",
              "1                                  -8                      0  \n",
              "2                                   1                     86  \n",
              "3                                   3                     91  \n",
              "4                                   0                     80  \n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "metadata": {
        "id": "Y7zqU4yyi4rB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "cc19dfd8-ced1-442e-ef02-c54866255785"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "target_class.head()\n",
        "target_class['num'] = pd.Categorical(target_class['RiskPerformance'], categories=target_class['RiskPerformance'].unique()).codes\n",
        "target = 1 - target_class['num']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "KRhVkk5E251J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 938
        },
        "outputId": "ec56254b-8d80-4765-c784-2b39eb297489"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(data, target, train_size = 0.50)\n",
        "\n",
        "# 'trueScore' is 'recall':\n",
        "# https://en.wikipedia.org/wiki/Precision_and_recall\n",
        "# trueScore = recall = (true positives) / (true positives + false negatives)\n",
        "def trueScore(model): #Measures how effectively model detects sparse credit fraud\n",
        "    total = 0\n",
        "    correct = 0\n",
        "    predictions = model.predict(x_test)\n",
        "    for i in range(len(predictions)):\n",
        "        if np.array(y_test)[i] == 1:\n",
        "            # total: will add both true positives\n",
        "            #        and false negatives\n",
        "            total += 1\n",
        "            if predictions[i] == 1:\n",
        "                # true positive: if predicted as positive\n",
        "                #                and it was positive\n",
        "                correct += 1\n",
        "    # returns 'recall'\n",
        "    return correct/total\n",
        "\n",
        "models = {\n",
        "    \"Decision Tree Classifier\" : DecisionTreeClassifier(),\n",
        "    \"Random Forest\" : RandomForestClassifier(n_estimators=100),\n",
        "    \"Decision Tree Classifier D3\" : DecisionTreeClassifier(max_depth=3),\n",
        "    \"Random Forest D3\" : RandomForestClassifier(n_estimators=100, random_state=0, max_depth = 3),\n",
        "    \"Stochastic Gradient Descent Classifier\" : SGDClassifier(),\n",
        "    \"Passive Aggressive Classifier\" : PassiveAggressiveClassifier(),\n",
        "    \"Perceptron\" : Perceptron(),\n",
        "    \"KNN Classifier\" : KNeighborsClassifier(),\n",
        "    \"KNN Regression\" : KNeighborsRegressor()\n",
        "}\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "for m in models:\n",
        "    models[m].fit(np.array(x_train), np.array(y_train))\n",
        "    score = models[m].score(x_test, y_test)\n",
        "    true_score = trueScore(models[m])\n",
        "    #scores.append(score)\n",
        "    #trueScores.append(true_score)\n",
        "    print(m, \"\\n   score: \", score)\n",
        "    print(\"\\n    true score: \", true_score,\"\\n\")\n",
        "    \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Decision Tree Classifier \n",
            "   score:  0.6250478011472276\n",
            "\n",
            "    true score:  0.6686195532771878 \n",
            "\n",
            "Random Forest \n",
            "   score:  0.7233269598470363\n",
            "\n",
            "    true score:  0.7766385939216405 \n",
            "\n",
            "Decision Tree Classifier D3 \n",
            "   score:  0.7065009560229446\n",
            "\n",
            "    true score:  0.7239106554375686 \n",
            "\n",
            "Random Forest D3 \n",
            "   score:  0.7173996175908222\n",
            "\n",
            "    true score:  0.7788355913584768 \n",
            "\n",
            "Stochastic Gradient Descent Classifier \n",
            "   score:  0.5116634799235181\n",
            "\n",
            "    true score:  0.1208348590259978 \n",
            "\n",
            "Passive Aggressive Classifier \n",
            "   score:  0.4824091778202677\n",
            "\n",
            "    true score:  0.0567557671182717 \n",
            "\n",
            "Perceptron \n",
            "   score:  0.5305927342256214\n",
            "\n",
            "    true score:  0.9264005858659832 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
            "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.passive_aggressive.PassiveAggressiveClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
            "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.perceptron.Perceptron'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
            "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "KNN Classifier \n",
            "   score:  0.6638623326959847\n",
            "\n",
            "    true score:  0.6367630904430611 \n",
            "\n",
            "KNN Regression \n",
            "   score:  0.08917520871402385\n",
            "\n",
            "    true score:  0.15854998169168802 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "K01Hq3LBcsyG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The 'true score' above identifies the ratio of correct predictions on the test set (applications that were 'Bad' and predicted as 'Bad') divided by the number of known 'Bad' applications - the numbers should be close to 62% for the Decision Tree and 72% for the Random Forest (results will change as the splitting of test and training data is random) and even better when we truncated the number of branches to 3 in both cases\n",
        "\n",
        "**Note** This is the point where the small amount of data (around 10k data points) works against the development of better predictors. As we try more and more models, we eventually might get one that has very good scores, but we do not know how overfitted it is. As I have mentioned in other examples, having millions of data points can crack the ceiling of traditional classification methods by adding Deep learning methods.\n",
        "\n",
        "A full decision tree can be find in the 'out.dot' file which will be donwloaded. Open the file with a simple editor, cut and paste the text into https://dreampuf.github.io/GraphvizOnline/ and you will see a huge Decision Tree (I attach a previous example but you might have created a slightly different one dur to the randomness introduced when selecting the training set)\n"
      ]
    },
    {
      "metadata": {
        "id": "YOIl2pJu5BoV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "   \n",
        "from sklearn.externals.six import StringIO  \n",
        "import matplotlib.image as mpimg\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "from google.colab import files\n",
        "\n",
        "\n",
        "export_graphviz(models[\"Decision Tree Classifier D3\"], out_file = 'outD3.dot')\n",
        "files.download('outD3.dot')\n",
        "\n",
        "\n",
        "# open out.dot in notepad, and use\n",
        "# https://dreampuf.github.io/GraphvizOnline/\n",
        "\n",
        "# Show a (previously computed) example of a Full Decision\n",
        "#Image('CreditCardFullDecisionTree.jpg', width = 900) \n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dn1I0vsUAPJd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "d70b826c-920f-44ab-d7ce-42173bb6e2b0"
      },
      "cell_type": "code",
      "source": [
        "export_graphviz(models[\"Decision Tree Classifier\"], out_file = 'out.dot')\n",
        "files.download('out.dot')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "contest.csv\tCreditCardDecisionTree.jpg\tdatalab    out.dot\r\n",
            "creditcard.csv\tCreditCardFullDecisionTree.jpg\toutD3.dot\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3_bB5rLdf257",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "That Decision Tree is way more complex than the 3 level one - and the 100 Tree Random Forest is even nastier. \n",
        "\n",
        "It would look like \"*No one really knows how the most advanced algorithms do what they do*\", but it is actually possible to go branch by branch and add the contribution of each variable - please read [Interpreting Random Forests](http://blog.datadive.net/interpreting-random-forests/) for the explanation.\n",
        "\n",
        "The library 'treeinterpreter' has the tools required to convert the ugly 100 Random forest black box into a nice explainable white box.\n",
        "\n",
        "Below I show the results of 5 known 'Bad' applications.\n",
        "\n",
        "If the amount (with the bias) adds to 1, it is considered a fraud, and the weight of each variable is shown (both for a Decision Tree and the Random Forest). \n",
        "\n",
        "When you run the code, you will see that the bias is just around 52% for 'Bad' credits, so the contribution of the remaining factors can only be up to 48% (that is why you will see lost of 25% total contributions).\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "5uNglHEJJ5lW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1988
        },
        "outputId": "703406e7-61a8-4325-f5e1-727d97287fa8"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "!pip install treeinterpreter \n",
        "from treeinterpreter import treeinterpreter as ti\n",
        "\n",
        "# all the instances are frauds (target = 1)\n",
        "instances = data[target == 1]\n",
        "predictions = models[\"Decision Tree Classifier D3\"].predict(instances)\n",
        "prediction, bias, contributions = ti.predict(models[\"Decision Tree Classifier D3\"], instances) \n",
        "#prediction, bias, contributions = ti.predict(models[\"Random Forest\"], instances) \n",
        "\n",
        "prediction_rf, bias_rf, contributions_rf = ti.predict(models[\"Random Forest D3\"], instances) \n",
        "print ('DC Bad credit initial bias',bias[0][1])\n",
        "print ('RF Bad credit initial bias',bias_rf[0][1])\n",
        "\n",
        "\n",
        "#for i in range(len(instances)):\n",
        "labels = ['feature', 'decision_tree', 'random_forest', 'value']\n",
        "labels_plot = ['feature', 'decision_tree', 'random_forest']\n",
        "colors = plt.cm.GnBu(np.linspace(0, 1, 30))\n",
        "\n",
        "# show 5 frauds and their analysis\n",
        "for i in range(5):\n",
        "    allocations = sorted(list(zip(contributions[i], contributions_rf[i], list(x_test))), key = lambda x: -abs((x[0])[1]))\n",
        "    alloc = []\n",
        "    for c, c_rf, feature in sorted(list(zip(contributions[i], contributions_rf[i], list(x_test))), key = lambda x: -abs((x[0])[1])):\n",
        "      alloc.append((feature, c[1], c_rf[1], instances[feature].iloc[i]))\n",
        "    alloc_df = pd.DataFrame.from_records(alloc, columns = labels)\n",
        "    fig, ax = plt.subplots()\n",
        "    alloc_df[labels_plot].set_index('feature').T.plot(ax = ax, kind='bar', stacked=True, color = colors)\n",
        " \n",
        "    ax.legend(loc = 'upper right', bbox_to_anchor = (1.5, 1))\n",
        "#    print (\"-\"*20)\n",
        "    \n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: treeinterpreter in /usr/local/lib/python3.6/dist-packages (0.2.1)\r\n",
            "DC Bad credit initial bias 0.5217058711034614\n",
            "RF Bad credit initial bias 0.5218875502008032\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Cly5dYvLkydy+fRt3d3ccHBweep0GDQzQ1dV56JgXWW7J3WcdgnhMJiZGzzoEIR5K/oyK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VSUlJTw73//G39/f06ePElwcDAuLi7MnTsXgOLiYpYtW4alpWWV9+/GjRuYmZkBZaPF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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4b54a40ba8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/27ZtIyIigm+++Yb09HQArl+/TkhICN7e3kRGRjJixAhMTU0JCQm5b2Jceb/c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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4b52254f60>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BwfTs2ZPg4GAWLVrE8uXL1XP079+fqKgoAH744Qe6d++ufi0qKgptbW3CwsII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\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4b5174d3c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7778nOTkZgJs3bxIUFISHhwcREREMGDAAIyMjgoKCyk2AH9wvFxcXhg4dyt27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\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4b51adf940>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Wvcyk5lg8z2b/kFbXIYgaWt3Z8tGNXmAvYs2x+N970BrO4unJGh5CCCGEEELc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\ni4iIiIg8oOJYREREROSB/wNRzUQ0JsqoSwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4b51febd30>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "GZw1ynxJNn7b",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Comments\n",
        "\n",
        "To be honest, I was surprised of how easy is to develop a model from scratch **once the data is all cleaned and formatted**\n",
        "\n",
        "But also, this example shows the danger of blindly using models without understanding the domain. \n",
        "\n",
        "The purpose of the contest was to **explain** the decision, but if it turns out that the data used to reach a decision is **unexplained** (External Risk Estimate: \"Consolidated version of risk markers\" - what is that ?) the whole exercise is tautological: instead of 'computer says no' we get 'because this [laundry list] and because another computer we do not control said no'\n",
        "\n",
        "\n"
      ]
    }
  ]
}