R-integration/SAS and R Integration for Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SAS and R Integration for Machine Learning "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook represents an example of how you can use SAS Viya with R for analysis.In this example, we will import R packages, start a CAS Session, load data from the local file system into CAS, explore the data, impute missing values, create several models in R, create several models in CAS, score a test set using our models, and assess model performance. <br> \n",
    "Further documentation on using SWAT with R can be found here: \n",
    "* [Getting Started with SAS Viya for R](https://go.documentation.sas.com/?cdcId=pgmcdc&cdcVersion=8.11&docsetId=caspg3r&docsetTarget=n0r11es31sftt2n19xherqhc44tj.htm&locale=en)\n",
    "* [Package Swat](https://developer.sas.com/apis/swat/r/v1.0.0/R-swat.pdf)\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "1. [Scripting Wrapper for Analytics Transfer](#Scripting-Wrapper-for-Analytics-Transfer)\n",
    "1. [Set Up the R Notebook for Analysis](#Set-Up-the-R-Notebook-for-Analysis) \n",
    "1. [Explore the Data](#Explore-the-Data)\n",
    "1. [Clean the Data](#Clean-the-Data)\n",
    "1. [Build and Score R Models](#Build-and-Score-R-Models)\n",
    "1. [Build and Score CAS Models](#Build-and-Score-CAS-Models)\n",
    "1. [Assess Models](#Assess-Models)\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scripting Wrapper for Analytics Transfer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SAS Scripting Wrapper for Analytics Transfer (SWAT) package for R is a R interface to SAS Cloud Analytic Services (CAS) which is the centerpiece of the SAS Viya framework. With this package, you can load data into memory and apply CAS actions to transform, summarize, model and score the data. Result tables from the actions are a superclass of data frame, enabling you to apply your existing R programming skills to further post-process CAS result tables.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Up the R Notebook for Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will load the packages we want to use. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SWAT 1.4.0\n",
      "randomForest 4.6-14\n",
      "Type rfNews() to see new features/changes/bug fixes.\n",
      "\n",
      "Attaching package: ‘randomForest’\n",
      "\n",
      "The following object is masked from ‘package:ggplot2’:\n",
      "\n",
      "    margin\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Load necessary packages\n",
    "library('swat')\n",
    "library('ggplot2')\n",
    "library('reshape2')\n",
    "library('rpart')\n",
    "library('randomForest')\n",
    "library('xgboost')\n",
    "options(cas.print.messages = FALSE)\n",
    "options(warn=-1)\n",
    "sink()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create our connection to CAS. Please see [this documentation on connecting and starting CAS Sessions]( http://go.documentation.sas.com/?cdcId=vdmmlcdc&cdcVersion=8.11&docsetId=caspg3r&docsetTarget=p0paczu3x2qu0wn1p94ees7y5ls8.htm&locale=en) for more information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NOTE: Connecting to CAS and generating CAS action functions for loaded\n",
      "      action sets...\n",
      "NOTE: To generate the functions with signatures (for tab completion), set \n",
      "      options(cas.gen.function.sig=TRUE).\n"
     ]
    }
   ],
   "source": [
    "conn <- CAS('localhost', port=5570, caslib = 'casuser')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to load our CAS Action Sets. Please see [this documentation on running CAS Actions](http://go.documentation.sas.com/?cdcId=vdmmlcdc&cdcVersion=8.11&docsetId=caspg3r&docsetTarget=p11zy53gvlofvsn1glguw9vuuhru.htm&locale=en) for more information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "actionsets <- c('sampling', 'fedsql', 'decisionTree', 'percentile', 'autotune', 'regression')\n",
    "for(i in actionsets){\n",
    "    loadActionSet(conn, i)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can load our data from a CSV file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "castbl <- cas.read.csv(conn, './data/hmeq.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us begin exploring our data. Just like in R, we can view the first few rows of our CAS data table using the head function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A casDataFrame: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>BAD</th><th scope=col>LOAN</th><th scope=col>MORTDUE</th><th scope=col>VALUE</th><th scope=col>REASON</th><th scope=col>JOB</th><th scope=col>YOJ</th><th scope=col>DEROG</th><th scope=col>DELINQ</th><th scope=col>CLAGE</th><th scope=col>NINQ</th><th scope=col>CLNO</th><th scope=col>DEBTINC</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>1100</td><td>25860</td><td> 39025</td><td>HomeImp</td><td>Other </td><td>10.5</td><td>  0</td><td>  0</td><td> 94.36667</td><td>  1</td><td>  9</td><td>     NaN</td></tr>\n",
       "\t<tr><td>1</td><td>1300</td><td>70053</td><td> 68400</td><td>HomeImp</td><td>Other </td><td> 7.0</td><td>  0</td><td>  2</td><td>121.83333</td><td>  0</td><td> 14</td><td>     NaN</td></tr>\n",
       "\t<tr><td>1</td><td>1500</td><td>13500</td><td> 16700</td><td>HomeImp</td><td>Other </td><td> 4.0</td><td>  0</td><td>  0</td><td>149.46667</td><td>  1</td><td> 10</td><td>     NaN</td></tr>\n",
       "\t<tr><td>1</td><td>1500</td><td>  NaN</td><td>   NaN</td><td>       </td><td>      </td><td> NaN</td><td>NaN</td><td>NaN</td><td>      NaN</td><td>NaN</td><td>NaN</td><td>     NaN</td></tr>\n",
       "\t<tr><td>0</td><td>1700</td><td>97800</td><td>112000</td><td>HomeImp</td><td>Office</td><td> 3.0</td><td>  0</td><td>  0</td><td> 93.33333</td><td>  0</td><td> 14</td><td>     NaN</td></tr>\n",
       "\t<tr><td>1</td><td>1700</td><td>30548</td><td> 40320</td><td>HomeImp</td><td>Other </td><td> 9.0</td><td>  0</td><td>  0</td><td>101.46600</td><td>  1</td><td>  8</td><td>37.11361</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A casDataFrame: 6 × 13\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       " BAD & LOAN & MORTDUE & VALUE & REASON & JOB & YOJ & DEROG & DELINQ & CLAGE & NINQ & CLNO & DEBTINC\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 1100 & 25860 &  39025 & HomeImp & Other  & 10.5 &   0 &   0 &  94.36667 &   1 &   9 &      NaN\\\\\n",
       "\t 1 & 1300 & 70053 &  68400 & HomeImp & Other  &  7.0 &   0 &   2 & 121.83333 &   0 &  14 &      NaN\\\\\n",
       "\t 1 & 1500 & 13500 &  16700 & HomeImp & Other  &  4.0 &   0 &   0 & 149.46667 &   1 &  10 &      NaN\\\\\n",
       "\t 1 & 1500 &   NaN &    NaN &         &        &  NaN & NaN & NaN &       NaN & NaN & NaN &      NaN\\\\\n",
       "\t 0 & 1700 & 97800 & 112000 & HomeImp & Office &  3.0 &   0 &   0 &  93.33333 &   0 &  14 &      NaN\\\\\n",
       "\t 1 & 1700 & 30548 &  40320 & HomeImp & Other  &  9.0 &   0 &   0 & 101.46600 &   1 &   8 & 37.11361\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A casDataFrame: 6 × 13\n",
       "\n",
       "| BAD &lt;dbl&gt; | LOAN &lt;dbl&gt; | MORTDUE &lt;dbl&gt; | VALUE &lt;dbl&gt; | REASON &lt;chr&gt; | JOB &lt;chr&gt; | YOJ &lt;dbl&gt; | DEROG &lt;dbl&gt; | DELINQ &lt;dbl&gt; | CLAGE &lt;dbl&gt; | NINQ &lt;dbl&gt; | CLNO &lt;dbl&gt; | DEBTINC &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 1100 | 25860 |  39025 | HomeImp | Other  | 10.5 |   0 |   0 |  94.36667 |   1 |   9 |      NaN |\n",
       "| 1 | 1300 | 70053 |  68400 | HomeImp | Other  |  7.0 |   0 |   2 | 121.83333 |   0 |  14 |      NaN |\n",
       "| 1 | 1500 | 13500 |  16700 | HomeImp | Other  |  4.0 |   0 |   0 | 149.46667 |   1 |  10 |      NaN |\n",
       "| 1 | 1500 |   NaN |    NaN | <!----> | <!----> |  NaN | NaN | NaN |       NaN | NaN | NaN |      NaN |\n",
       "| 0 | 1700 | 97800 | 112000 | HomeImp | Office |  3.0 |   0 |   0 |  93.33333 |   0 |  14 |      NaN |\n",
       "| 1 | 1700 | 30548 |  40320 | HomeImp | Other  |  9.0 |   0 |   0 | 101.46600 |   1 |   8 | 37.11361 |\n",
       "\n"
      ],
      "text/plain": [
       "  BAD LOAN MORTDUE VALUE  REASON  JOB    YOJ  DEROG DELINQ CLAGE     NINQ CLNO\n",
       "1 1   1100 25860    39025 HomeImp Other  10.5   0     0     94.36667   1    9 \n",
       "2 1   1300 70053    68400 HomeImp Other   7.0   0     2    121.83333   0   14 \n",
       "3 1   1500 13500    16700 HomeImp Other   4.0   0     0    149.46667   1   10 \n",
       "4 1   1500   NaN      NaN                 NaN NaN   NaN          NaN NaN  NaN \n",
       "5 0   1700 97800   112000 HomeImp Office  3.0   0     0     93.33333   0   14 \n",
       "6 1   1700 30548    40320 HomeImp Other   9.0   0     0    101.46600   1    8 \n",
       "  DEBTINC \n",
       "1      NaN\n",
       "2      NaN\n",
       "3      NaN\n",
       "4      NaN\n",
       "5      NaN\n",
       "6 37.11361"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(castbl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visual exploration helps us better understand patterns and distributions within our data. We can use ggplot to look at our data distributions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p4BusCoRPJ/phB/Ln/OkJwX73uVsTc6XmaXPldxPN5PjKeEbn2QUcOxJ9/Y3yUd\nQ5VHz+IiVywPJR4bkqRO5Cs+PFkud/yO3ktvsr4N98L/oEbgOxCU58zsLjEkb/Ihw6GRjuGw\nPINN25ftD51XY/t1d5+3mmn+GTXOQECBUYnk+0wh97k/RuVZHOkaz3fcc/0VfeLPPvx5vJ8Y\nTw7tvbLAqOEMXJEqlUdVKOOrWB5KODYsSZnIX3xoslLuDXfEc9/ryTF+oapH2jsQlOffNmOJ\nOnnx+67Jh+ft+3TGFElnXo3t19h9p31Qc/99M+qcgYACoxLJ95lC7nN/jMoziV97P5tcdncs\ntn5ZMiiP9xPjSaHSZx5n5HDMJ1Kl8ogKZfwVy0MJx4YlyRMJxYclK+X+umOcsfLl5o9O7QtV\nPdLagcA837aZSwzPK32q4+ZT4XmJ3kPMEElnXo3t19l9p71Xc/+9GbXOQECBkYnkfaaQ+9wf\no/IMXrv6Pud31dOL9lTIs/qJ8aTQ3hVFL12q0BOpQnnUzCJCxfJQ6rEVkuRkofjwkaVy99rv\nlXcOLl68qHPx4jXEMq0dCMzzts1aok7eC5uuToTmbd3KTJE0xtPYfq3d9w+hkeala52BgAKj\nEsn/mUL8ufw5Q3IeY4Pdu9yeax8MzrP7ifGk0IZtvnSpQlekSuWRM/sRK5aHIo4NTpKTheI1\nRpZW05GzXudOntx77cmTp4hlWjsQmOdum73E8DyD0uLdYXl398YtkbTGC9t+vd0XhghP89K1\nzkBAgVGJ5P9MIf5c/pwhOY+9uMT6z3Gv3FtgLLNoICiP9xPjiaGk9RaQGo75RKpUnjKkhFSx\nPJRwbFiSNJFYfEiyWu6rHS8Zr06YIfvWST3S2oHAPD6zs8SwvPuvyhq3akt3h+V9bmF3d3fn\nZX2h82psv+buO+0Tmvvvzqh3BgIKjOzb3/wzhfY84j2XPmdIzstd9QPzW46ZePeWEyN9K7NB\neW4/MZ5/WjbYYX57nxxufHRPlzFVaHlM+XQkP0rF8lD+Y0OTxImk4isnE+Wu+/wbxUcvNb+N\nbF+o8pF8B4LynJnjzhLD8k4tu/3VE9sWngjLs379rWfPVFheVl7PNHbfbqd095+3mmeALjAy\nkfhnCt1xq/dc+pwhOW+Q/whs+NbFPRt/Ezwe7yfG86expzqtD76lhrvSmurh0PJYYNhEqVge\nSjg2LEmcSCq+cjJR7vhXFl/2+cNEh9PyHQjMs2d2lxiSx1754qLLPjdYYV7fabC+/a2RV3n7\ntXffaXX3n7eaZ4AuEP9FCIAIgEgARABEAiACIBIAEQCRAIgAiARABEAkACIAIgEQARAJgAiA\nSABEAEQCIAIgEgARAJEAiACIBEAEQCQAIiA6kV6LNSVu/fUupDbYbKn/eL0rqQ2IxJktF2Kz\n1w+RIFI9gUj1BSJxZsuF2Oz1QySIVE8gUn2BSJzZciE2e/0QCSLVE4hUXyASZ7ZciM1eP0SC\nSPUEItWXhhfpH+b1m829b3ssFtv7mffPO7vz2+brX7a1tf0w0omiuxAvbGs7/d2fvPcl+6nJ\nB5xn53b9xEzwreNn17TPO/ej9057zghFosu/1u7s7TKev33AfDp/c4wv5TvTnTNqkZ79zPnz\nzrt0Z8wrPBY7MO99Q9YTb8+99U2bhhcptvgjR2Kxg+esj8X6z77gW4/96G9Pv9WIHhn4l8YV\n6Ypnn/nhbWddbtR94fIBk71W8NlnH+5+59PCOh47Z/43H/vx2nk3Rlf+9EUiy/eLdM4nzKem\nSP3vnm8t5QvTnTRakR4/19jWHcvnbvOLdNsn3/tPZuvbc29906bxRdr/7i/HYssuNv4xufjC\nF83Al063vkY927giWedu19yv+U+j/WzonV8R1vGxC6yn2285Mt1JIxQpuHxHpNvevTVmi/RX\nH7bq32CfkukQqUif+LNfmc3nb/Yt4cj5W6+7xHzi23NvfdOm8UWKfe3Mp79n3tg91bbdej10\nrvXvd6OLFLv0EvVKPHLWl/3r2N/29WnP5hC1SGT5jkhf2Xr2AUukZ9q+ZUWHzpn2l9QoRTrY\n9lX3ubuEb585+NMznjH+YfbteWuJFLv0E+cbN3axB9oG7NcfX2I+NrxIN37QeDr3TJM7nOCh\nz8572r+OH7X9dNqzOUQuElF+zBHp9tjf9FgiuUv52JLpzhqlSD9pe8R97rpyyZWx2IcN3/17\n7q1v2jSDSPvOush8l/hA2+P2679eaj42vEg3fMi9CX/eOWdtFzwgrOMh64yfNXfu3PunO2nk\nIknliyINvP27pkjfa3vCDgUMEs4AACAASURBVDeaSD92n/PCnz7jR7HY7ecNxfx77q1v2jSD\nSLELrzMff9b2D9aroXNuMZtn23ZEOknkIn18sXBvZJyzXWeb//b51vHc6XcbT/b0979r+3Qn\njVwkqXxRpNgXzh80RHq67RtWdOg9N0131ihF+uXpm6126CWv8BvazjrrrHe2bYv597zFbu0c\nkWIfm3/YbG6fO/D3a2Kx3fwf9oiIWqTt5lsh6Ur82jsei/nXEbvkA/9uPj3SeCJR5cc8kYbm\nX/dh42q9+C+s7yjfOW/a3/iKUqRY1/nWF5kb/8Yt/MX33PyMweWfjPn3vEVFevycC+5/8pHr\nz/hS7Gtz7+2/5ENDkU4SoUhXPPvsQzfOvTLm3hsNDDnn7PILDvvXEXv6vA9+44l/vfcj/30g\nsvKj+PY3Ub71bL8rUuzhue8xRNr1rot37Ntz49wvT3fSaEUaeE/7fY/tWPH2B/gS9n993s/N\njh+ePuDfc29906aZRIrtvfr8uWcv+L7x7At//M7OJ6KdJEKR2trazvzLr/KnJo87Ih18r7kU\nbx2x/X/3gXlnXXjr9O/SIxSJLN9ql3gixa5rM++fnug+p+0dix6Y7pxR/0D2Z1f/8bzzLje/\nq+AU/tEeu+NDq/177q1v2jSFSG8K0YlUF6ITqVqu+VAUo0Qr0psPROJApBrpn3vjwUPTHgUi\nQaSGoH4ixf7x/W+7c9qDQCSI1BDUUaRIgEgQqSGASPUFInFmy4XY7PW3gEjXdRgsYixx1/Lu\nDSfVFiLVEYhUX6oRaeWu0dHRMcY2rj12fPOqktJCpDoCkepLNSJd+pzVjHYOG1+FFgzKLUSq\nJxCpvlQhUr7jnuuv6Bth+xaWjVd/t0NujYeTjxscrveaamO2XIjNXv/sF2ly2d2x2Pplyf4V\n5qt12+TWeBhoNzhQdCmXiwpUqKSESkQo4jTxdc5dZ+WhqPp186jtIEqrKY8R9VPj8OGCe6jF\nBNcQ1UHev9derEL9tS2AOik6k1XYR2eyfBUiWaQX7elf6QgktcbDf3zb4FjShRWTMmkixApK\nLKuGMiynxHJEiGWVWJ4IsbTwOuEu0VcEMWOyrIbyLKPEiHUmCyylxLJ5Na9EzUGU4t9eRtRP\nzce71Ir5RMRieJe6jQ7UpvMuonAbYXvd8n0ls1LQocmMen1o1JIspgO7qE13oM4lr8NZg3f5\n6H77+9oHD9i3cjvllmdMjrqw/KjMOBFiGSU2RYRYSoklk2qIxZVYekoJZdiEGHBX6IXiTB1+\ntKSGUkwdn1jnaJaNKbGptJpXLBNzJNSYf3sZUX+WjasHOV2TQT1pFtyl7qwDtem8i9hDG2F7\n3fJ9W1kuBB06OpkN7EoE1zKamwjsKhIn1oE6lw5TfA1ViPTKvQXGMosGxjqPGsvtOiy3EMkP\nROJdEEki3r3lxEjfyizbtPrYyPo1ZaWFSD4gEu+CSDLDty7u2fgb4y52S+/SvnG1hUg+IBLv\ngkg1ApFMIBLvgkgQCSKJXRBJBiJBpAAgkgJE4kAkpRSIJAGR5BBE8uaDSCIQaRQiqTGIRAGR\nhAIhkjQHRPIDkSCSCESygUgQCSJBJIjkApFsIJICROJAJKUUiCQBkeQQRPLmg0giEGkUIqkx\niEQxa0SKj7uwwrjMFBFiWSWWIEIsrcRSaijNkkosm1BDbEp4PebWzyOn2cgHlpShxjNMHX9S\nXed4jk0qMWKh48WyGkun1Jh/e5lSPz0f74oH9WRYcJe6sw4pYtN5l3qKHJL+E0pdPuVi0KHj\n8VxgV4VaxvNTgV0lYtMdqHPpwC9K7/KJTqRs3oWV8zIFIsRKakwNFVlRjREhIq1EhFhBeJ1x\n6+cRRyT5QLV+ZSwTYp1kXlFdaL7M1BixUmF7mVI/PR/vCu6pcBBRg1Mdsem8S+8gzcuHQ1wf\nGrVUWDa56Xyy4Dr4ZNkZEGm23No5IskH4taOALd2EIkDkWwgkgJE4kAkpRSIJAGR5BBE8uaD\nSCIQaRQiqTGIRAGRhAIhkjQHRPIDkSCSCESygUgQCSJBJIjkApFsIJICROJAJKUUiCQBkeQQ\nRPLmg0giEGkUIqkxiEQBkYQCIZI0R9UiBdTvlAKRJCCSHIJINhBJpWFEeqJjP2OJu5Z3bzip\nthDJB0TiXRBJZWLZQkOkjWuPHd+8qqS0EMkHROJdEEll0/Zl+9lo57DxVWjBoNxCJD8QiXdB\nJIV9n84YIu2z/2bsDrmFSH4gEu+CSDKJ3kPMEKl/hfli3Ta5NR6e6zH494ILKxdkikSIldQY\nESLSSkSIFdUYEZLSvN8V5hHnQpQPZHKAnpJYZ6FMHEsslMwjVipsL9Ot35lCrZhPRCyGd9V0\nEFG4jXBC3fJzvhixh/zY4K4KtVRYNrnp4ZPxNeSqEWnrVmaJtNIRSGqNh4F2g4PhIzUgBSXi\nXIh1qCUamrR+9UQ0A0X3WbhIh3rjlkgH7Fu5nXLL83BrZ4JbO96FWzuJOxZ2d3d3XtY31nnU\nWG7XYbmFSH4gEu+CSBJWeT17ptim1cdG1q8pKy1E8gGReBdEojBu7VhqS+/SvnG1hUg+IBLv\ngkg1ApFMIBLvgkgQCSKJXRBJBiJBpAAgkgJE4kAkpRSIJAGR5BBEsoFIKhBpFCKpMYhEAZGE\nAiGSNAdE8gORIJIIRLKBSBAJIkEkiOQCkWwgkgJE4kAkpRSIJAGR5BBEsoFIKhBpFCKpMYhE\nAZGEAiGSNAdE8tMKImU8WCkjkyVCrKjEckSIFZRYXg0VWF6JFYkQywmvU0r9zoUoH1hWhjKm\nzCkxYp3GnFklllcXmilTc6hLELZX3f+A+p1S1Ir5RMRiKtRgkyc2nXepp8g9yNdFXj7EPjgQ\n14dGLRWWTW66A3UueR3OGrzLJzqRknEXVozLJIkQyyuxNBFiWSWWJUIsrcRyaijPUsLrSbd+\nHnEuRPnAsjJUPEdMSawzXmAJJZbOqXklYo5sRo35t5fp1m+TT9FxczHBXUQNTnUsuEs9RQ7C\nCaUun3Ip6NB4Sr0+OJngWuKFwLWRm+5AnUsHvoapGRAJt3YmuLXjXbi1g0gQSeyCSDIQCSIF\nAJEUIBIHIimlQCQJiCSHIJINRFKBSKMQSY1BJAqIJBQIkaQ5IJIfiASRRCCSDUSCSBAJIkEk\nF4hkA5EUIBIHIimlQCQJiCSHIJINRFKBSKMQSY1BJAqIJBQIkaQ5IJKfphbp1Y3dS245wlji\nruXdG06qLUTyAZF4F0SSKCy/e+T1LZen2ca1x45vXlVSWojkAyLxLogkMflQmrGRjuHRzmHj\nq9CCQbmFSH4gEu+CSATx+67J77P/+PIOuTUexg4avD7pwgqTMgk1FGc5JZYiQiyjxDJEiKWU\nWFYN5VhCeD3h/YPh4FyI8oFlZajJLEsqMWKdk3kWV2KprJpXouZIqzH/9jLd+p1S1Ir5RMRi\neJe6jQ7UpvMu9RQ5CCfULd83e7kYdOhkMh/YlWbEVjkUEoFd1KY7UOfSwV1DVSKVPtVx8ynW\nv8J8vm6b3BoPA+0GB7WUbDTUv0rvXIh1qCUamrR+9UQ0A0X3mdZXpNde2HR1on+l+dQQSGqN\nh+F7DH6ddmGltEyGCLGiEssWlFCO5ZVYngixnBIrZNUQE2NJd5E84lyI8oFlZShjLHVKYp3p\nIssoMWKh6TIxR16dQtheplu/U4q6Iw7yxvi7iBqc6ogd4F3qKXIQTqhSfpq8fDhZ9ZLRqKXC\nsslNd6DOpQNfg3f5aH77u7R49wH7Vm6n3PIUvEcywXsk3tXK75Hah+z2X871BZ+/KstYeenu\nsc6jxnK7DsstRPIDkXhXK4s05zmrKWz433zBxLLbXz2xbeEJtmn1sZH1a8pKC5F8QCTe1boi\nzfF4v1+vV7646LLPDTKW2tK7tG9cbSGSD4jEu1pXpMGvzum60uTTX3iNVQ9EMoFIvKt1RWLs\noy/XIBBE8gOReFcrizQtIJIJROJdrSzSyeVtv22/SYJI8oEQiQAi0SIt+t2PLLfeJV0JkeQD\nIRIBRKJF+oOf1CAQRPIDkXhXK4v0+29AJIikdkEkmRCR/vwpiASR1C6IJBMi0i/+ZB9EgkhK\nF0SSCRFp/tw5v/82C4gkHwiRCCASLdKff4QDkeQDIRIBRKJFmhYQyQQi8S6IBJEgktgFkWTC\nfo7EeWsNIk2dcmH5UzITRIhllVg8o4ZYSokliRCLK7EMEWKTYsCtnwecC1E+sKQMdSrtX7MD\nsc5TOTauxIiFniqW1VgqqcZYwfdct36brFqxA7UY3pUI6kmy4C71FDkIJ9Qt33eqysWgQ09N\nqZeMRi2ncpOBXdSmO1Dn0sFdAy1Sl8WfvOW8VTWIlCu4sHJBpkiEWEmNESEirUSEWFGNESEp\nLevWzyPOhSgfyOQAPSWxzkKZOJZYKJlHrFTYXqZbvzOFWjGfiFgM76rpIKJwG+GEal4+7rHB\nXRVqqbBsctPDJ+NryNEiOZz4i901iIRbOxPc2vGuVr614zzXDpHkAyESAUSqLNKJt0Ak+UCI\nRACRKopU/vIZEEk+ECIRQCRapPdZnPeHcz4HkeQDIRIBRKok0vkXfzXHqgcimUAk3tXKIk0L\niGQCkXhXa4t0ave2b/XHIRJE8ndBJJkQkUo3/J75gQ3/5Q6IBJF8XRBJJkSkO+Zcsv3R3d/4\n6JzvQCT5QIhEAJFokc5dY7dXC5+0CpFMIBIBRKJF+k9P2u1P8QNZiOTrgkgyISL9l112+5P/\nHSLJB0IkAohEi3TBRdYPkDJ/fSFEkg+ESAQQiRbpp7817zMb//6qtt9+3B8d29xz2U0xxhJ3\nLe/ecFJtIZIPiMS7Wlkk9uNzzG9///efCsHPrh1+/c6lGbZx7bHjm1eVlBYi+YBIvKulRWLs\n+M+f+40Yife9ytgbHS+Pdg4bX4UWDMotRPIDkXhXS4t04h7j4Q3f/RrnSNf4Pvtvxu6QW4jk\nByLxrlYW6aX/Zv7Ny1fm/LdhyaP4tfez/hXms3Xb5NZ4GLzG4Fd5F1bOK6ihAiupMTVUZEU1\nRoSItFJBDTExlnHXyCPOhSgfSCxJHquaPGKh+TJTY8RKhe1luvU7pRAVBxfJu4ganOqITedd\nege55Wd9MWIPHahtC6+lwrLJTXf7QifzPqlAEGnBu35uNkPv+pTo0WtX31dm/SsdgaTWeBho\nNzjImpGCEnEuxDrUEg1NWr96IpqBovtMEOm//pPdfkP8FKHBbvPnSwfsW7mdcms8FKYMfJ+U\nM41PESJCxKcIpVrmU4TSDfApQplaPkWIOEUOLfApQm95wG6///v+6ItLfmE2Y51HjTvZrsNy\ny9PwHskE75F4Vyu/R/r/P2p9qYp/cL4vmLvqB+YRGbZp9bGR9WvKSguRfEAk3tXKIvX/1pmr\n1n9h5X/97X5fcLDDYjdLbeld2jfOlBYi+YBIvKuVRWJ72s0fyP6R+ANZTSCSCUTiXS0tkvGW\n9YUXa/sFWYhkAZF4V4uLVDsQyQQi8S6IBJEgktgFkWQgEkQKACIpQCQORFJKgUgSEEkOQSQb\niKQCkUYhkhqDSBQQSSgQIklzQCQ/EAkiiUAkG4gEkSASRIJILhDJBiIpQCQORFJKgUgSEEkO\nQSQbiKTSVCKlPVgpLZMhQqyoxHIFNcTySixPhFhOiRWIEMsKr5NK/c6FKB9YVoYyxlLHJ9aZ\nLrKMEiMWmi4TcxArFbZX3f+A+p1SsnRc3Rh/l7pMXh2xA7yLKNxGOKGalw8nq14yGrVUWDa5\n6Q7UuXTga/Aun+hESsZdWDEukyRCLK/E0kSIZZVYlgixtBLLqaE8SwmvJ936ecS5EOUDy8pQ\n8RwxJbHOeIEllFg6p+aViDmyGTXm316mW79NPkXHzcUEdxE1ONWx4C71FDkIJ5S6fMqloEPj\nKfX64GSCa4kXAtdGbroDdS4d+BqmZkAk3NqZ4NaOd+HWDiJBJLELIslAJIgUAERSgEgciKSU\nApEkIJIcgkg2EEkFIo1CJDUGkSggklAgRJLmgEh+IBJEEoFINiEiyYuBSBBJBCLZVCNSxZVB\nJLFAiCTNAZEgUkhREEkCItlAJA5EEoBIAhAJIolAJIgEkTgQyQYiKUQu0sgNXWaTuGt5t/l3\nmuUWIvmASLwrSpEEAoZtfJH29m6xRNq49tjxzatKSguRfEAk3gWRZJ58Y78p0mjnsPFVaMGg\n3EIkPxCJd0EkFUukffYfX94ht8ZDfMjg5IQLK0zITKmhOMspsSQRYhkllk6rIZZUYlkixOLC\na+8vDvKIc7rkA0vKUBMZllBixDon8mxKiSWzal6xTMyRUmP+7WW69dvk1IodssRieBdRg02a\nBXepp8ghyXxdbvm+2ctF9yklUvW1TOTjgV0lYtMdqHPpwC9K7/LRF6l/hfl03Ta5NR4G2g0O\n6ozUcKh/ld45XXWoJRqatH71RJhQIr3JhVWk6D6rQqSV5lNDIKk1HmJfNng548JKGZksEWJF\nJZYjQqygxApEiOXVWE4JFZkYS7mL5BHndMkHlpWhjCnV8Yl1GnNmlVhOXUGmTM2hrkrYXqZb\nv1OKWjGfiFhMhRps8sSm84OIBfKDfF1K+WbM2wdKpOpryZQC10ZuugN1Lh34ReldPvoiHbBv\n5XbKLU/CeyQTvEfiXXiPFCDSWOdRY7ldh+UWIvmBSLwLIsmMj+7pMq/ATauPjaxfU1ZaiOQD\nIvEuiCRzZYfJwyy1pXdp3zhTWojkAyLxLohUIxDJBCLxLogEkSCS2AWRZCASRAoAIilAJA5E\nUkqZBSIFrG+2iiStFyKJQCQbiASRIBJEgkherRDJAiIpQCQORFJKgUgSzSISXzVEEnmTRJIX\nA5FkIJIcgkg2EEkFIo1CJDUGkSCSUCdEEoFINhCJEySSGINIEhDJBiJxIJIARBKIRiTlNM0a\nkfJFF1YuFoXlGjEjJMfMNIkSEWIlNUaEiLQyEZLScm79POKVLMDkgDqWHaTy1Bix0CKj8ogp\n/PvGKtcvL4bYEbfI4K7AHmrTeZfeQW75OV/MW1+oSFq1VFgBuenuYYE9fLL8DIjk/oNG/ruB\nr0gi+Ipk45aPr0i1ikResBBJngMiQaTZIJL7AiIRQCSIxIFINhBJASJxIBJEspg1ImnthFUg\nRJLmgEgQCSLZQCQBiDSTIjkvIJI8B0SCSBDJBiIJaIlU5eUzCpGog6SaIZJUctUiuSGIJAOR\n5BBE8lfcKiJ5S4NI6kFSzRBJKhkiQaRqRLJfQCQZiERdRa0uUsC2BAzk9NRVJDcBIkGkKERK\n3LW8e8NJiCTQBCKJC+RAJIU3S6SNa48d37yqVA+RxFcWEImqX948iCRTf5FGO4eNr0oLBmdA\npKo3cRQiVS2SsEyIRPAmibTP/huyO1pcJCEbInldtYo07SuBoKFF6l9hPlp/1fzwWoMjWYdp\n7EQEZH0U81mZIssJr9PueqTyhVfGk3JWSSiyvC/BJlfKKrGSNKdJvqiEsmWmxooFNcZKvueV\n65e3ppRXY0EHVazBpsDUHeZdgQflma/LLT8j1T/dcy9QCiwzWy4HduVKgV18Dd5fZZ+GSCtd\nkQbaDQ7WPFI9KdS7AGDTnCfC+8yJ2kU6YN/a7TQe0iMGo+MurDAuM6mGplhWiSWIEEsrsVRK\nCaVZUollEkooy6aE12PuerxYkphxvKSGMkwdn1jneI5NKrFERs0rltVYWl2psL2MqJ+aj3fF\ng3oyLLhL3VmHFLHpvIso3EbYXrd83+zlYtCh4/FcYFeFWsbzU4FdJWLTHahz6cAvSu/yqV2k\nsc6jxp1t12H+erb8oTHpJp7TND+QNecbVw9yuprl50gBTGYDu5r2mw1s0+pjI+vXlCGSH4jE\nuyCSLqktvUv7vK/NEMkEIvEuiFQjEMkEIvEuiASRIJLYBZFkIBJECgAiKUAkDkRSSoFIEo0s\n0slH/02ZeUy14bVHn1NiE+p18+tHX1DXoV7BLz76shJLqPv3y0f/QwwQF+LLj76oHDeqGj76\nwqPHlBixztEDj76uxIiFjj61R41NqZf3G48+672gRPr5o8fVgWySgYodevR/BnXFAxWjNt2B\nOEUORx897L2gROp/OujQ0fFAPUeHHn0psC942aNPPR7YRZ1Lh18/+iv7yQyI5JFvv0onbbh9\ng07aM+3bddIeaN+jk3Z7+5HQnD3tD+gMxb7Rvk8r7/PtJ8OTDBZeqJVWbL+ycsK69hGtgQS+\n2v589Qc92N5f/UFPtH+3csL8y6sflLGd7btqOWzBR2o56un2f5IiEEkFIukCkVwgkgpE0gUi\nuUAkFYikC0RymQmRykOv6KTlhl7XSYsPndJJGxua0kk7MZQJzZkaGgvNMXljKKmVNzKk91+b\nh2NaaaHbe3woVzmB4uRQqvqDNDddJHR7XxquflDGxocmazns13qbLqFelDMhEgAtB0QCIAIg\nEgARMAMiSR/TRTJyQ5dG6tjmnstuioWlvbqxe8ktR3TmfaJjf8U0f1eFNF6WyXUdBovo6fx9\nFYZ7ocNid+XxiB2jx9TZ/oql6qF3BgOOqjRd1fULZ1Xz6Nf+funim16s7jCndvmidNoZEEn6\nmC6Kvb1bujRSP7t2+PU7l2YqpxWW3z3y+pbL0+HzTixbuL/inP6uCmm8LJOVu8wfgdPz+fsq\nDGf9R5QXF71acTxqx+gxNba/cqlaaJ7BgKMqTVdt/eJZ1Tu6fNW9qewDi+LVHMZrly9Kp41e\nJPljuiiefGN/V3hqvM+4uN7oeLly2uRDaeNfi47h8Hk3bV+2v9Kc/q4Kabws68WlzwXP5+sL\nLe7WB5VjBIgdo8fU2f6KpeqhdwYDjqo0XdX1C2dV8+jJDuMOZrwjVs1hTu3yRcnb6EWSP6aL\nxipKJ/VI13h4Wvy+a/KhWfs+nTG2vEKavytsNKMss8l33HP9FX30j238fWHD7b2yoBwjoewY\nPabe9lcoVRftM6geVWm6akcUz6ru0Z/fEs88+OlcdYfZ/wiY+C5K3kYvku9jusKK0kiNX3t/\naFrpUx03nwrNSvQeYsaWV0jzd4WMZpZlMrns7lhs/TLy50n+vpDhSp95XD1GQtkxeky97a9Q\nqi66Z5A4qtJ0VY4onVXdo8dWdXT0/rrKw1yR/Bclb2dAJO9jusKKCk997er7yuFpr72w6epE\nWNbWrcza8uA0f1fl0ayyXNKLgv9ThdMXUtzeFUXfK3o8ZcfoMfW2v0KpumieQeqoStNVOaJ0\nVjWPLlx/72Rq59Lx6g7jtQsXJW+jF8n3MV1hRYWmDnbv0huxtHh3SNah3ri15RXS/F0VR7PL\n8rj2weDK7L6Q4jaIp5AcT9kxeky97a9Qqi56Z5A8qtJ01Y0on1XNo5/vNL9VdMUj1R3m1C5e\nlLyNXiT5Y7oqFBWW+uKSX4SP+PxVWcbKS3eHDHbHwu7u7s7L+iqk+bsqjeaUZfLKvcZ7m8yi\nASrN31e5uKT7NrfCeMqO0WPqbX+FUnXROoP0UZWmq25E+axqHv3LDvP/Q/U+Ut1htkjSRcnb\nGfj2t/QxXRTjo3u6zN9WrZyau+oH5neGQ9ISy25/9cS2hSdCBrN+fbJnz1SlNN6155FKq3DL\nMtLi3VtOjPStzBJpXl/l4QwGO6wfYVQaT96x4DE1tj+4VG30zmDAURWnq2pE5azqHZ3qvTeR\n+9HC16s5zKlduSiddgZEkj6mi+JK60eQD4ekDvKfVFZOe+WLiy773KDWvMZNQKU03nXHrZXS\n3LLMtOFbF/ds/A09G++rPJzBU53W9+wqjSfvWPCYGtsQXKo2emcw6KhK01Vfv/+sah79yoal\nl9/4QlWHObUrF6XT4r8IARABEAmACIBIAEQARAIgAiASABEAkQCIAIgEQARAJAAiACIBEAEQ\nCYAIgEgARABEAiACIBIAEQCRAIiA6ER6LdaUuPXXu5DaYLOm/iYHInHqXUhtsFlTf5MDkTj1\nLqQ22Kypv8mBSJx6F1IbbNbU3+RAJE69C6kNNmvqb3IgEqfehdQGmzX1NzkQiVPvQmqDzZr6\nmxyIxKl3IbXBZk39TQ5E4tS7kNpgs6b+JgcicepdSG2wWVN/k9OIIl349gGzmb85FuvtEl7G\n9n7m/fPO7vxOZFPNhEjPfub8eeddutN4duG1dkRdwbejmoxFUf+FbW2nv/uT975kPzX5gP3s\nrIu/HostsUNtK5Wz4a00gvqbnIYU6ZxPmI0rkvey/93zv/XYj/729C9ENlf0Ij1+7vxvPrZj\n+dxtPpF8Kzj7AmsFt0Y0G4ui/guvePaZH9521uVHjKfLB0z2WsFn+9e0PRLbPzCwve2BgYED\n8tnwrTSC+puchhTptndvjflE8l7+1YdfNBM2nN4f2WRu/VEN+Ik/+5XZfP5mn0jeCi6+0FrB\nl6JaAYuifrvOXXO/5pXsPBs6417z+SNtj5uNdDZ8K62ZyC6/etOQIn1l69kHfCK5L59p+5aV\nMHTOjZFN5tYf0XgH277qPndFclfwVNt2KzJ0bkQrYFHU79R56SWySIf7zt5nPveL5K7Fv9Ka\niezyqzcNKdLtsb/p8YnkvnygbcDO+NiSyCZz649ovJ8YN0McVyR1BR9fEs10LIr6nTpv/KDx\ndO6ZJnfYz04/936rxy+Suxb/Smsmssuv3jSoSANv/65fJOfl99qesDMaWqQfu899IjkreMC+\nImOxv14azXQsivqdOm/4kPse6Xn72b/edfadZo8okrMW/0ojqL/JaVCRYl84f9AnkvPy6bZv\nWAlD77kpssnc+iMa75enb7baoZcEkZwV/KztH+zec26JZjoWRf1OnR9frLxHit12nvkoiuSs\nxb/Smons8qs3jSrS0PzrPuwTyXl58V8MmQl3ztsb2WRu/VEN2HX+82Zz49+IIjkr+Nj8w2bk\n9rkD0czGoqjfrnO7+f5NFunWd5iPkkjOWnwrrZnILr9606gixR6e+x6fSM7LXe+6eMe+PTfO\n/XJkc0Uv0sB72u97SUBF/gAAGERJREFUbMeKtz/A75P2+1fw+DkX3P/kI9ef8aWIZmNR1G9+\np/uhG+deGXNv7QaGrOBT3zz3GjNBEslZi2+lEdTf5DSsSLHr2vwi2S9jT3Sf0/aORdM5czJu\n/ZGN+LOr/3jeeZf/NMZ/urlEWMHeq8+fe/aC70c1GYuifrPOM//yq/ypyePWs3kfvMW6BZBF\nctbirTSC+pucRhSpMtd8KNLh3PojHfVNg82a+puc5hOpf+6NBw9FN5xbf3RDvpmwWVN/k9N8\nIsX+8f1vuzO60dz6oxvyzYTNmvqbnCYUKVrc+utdSG2wWVN/kwOROPUupDbYrKm/ydEQ6boO\ng0WMJe5a3r3hpNo6QKR6wGZN/U2Ohkgrd42Ojo4xtnHtseObV5WU1gEi1QM2a+pvcjREuvQ5\nqxntHDa+Ci0YlFueBpHqAZs19Tc54SLlO+65/oq+EbZvYdl49Xc75JbnQaR6wGZN/U1OuEiT\ny+6OxdYvS/avMF+t2ya3xsPPLjLYU+9zUhvuOutdSG2wWVN/k6P5Xbv0oj39K80nhkBSazzs\n7zQ4VHRh5SJFiQ5XmV5iJTJerjLdDufcJYYeQk8QlExHq0umolIyI+qnDiSmoCqn9pw6PdQM\nerNKWxDVdVx3dL/9fe2DB+xbuZ1yyzMmR11YfpRiPCDMMmR8KiDMUmQ8maTDLE7G01N2667Q\n64ozcih6goDx+fAiGTZBhXN0lI1R4YIQZUT9WTau1qNWGWcJJUbs+RjLqUWUiLqYGiMWlvJf\nKP76m5xwkV65t8BYZtHAWOdRxqa6Dsstz4NI1PAiEKmFRYp3bzkx0rcyyzatPjayfk1ZaR0g\nEjW8CERqYZHY8K2Lezb+hrHUlt6lfeNq6wCRqOFFIFIri6QJRKKGF4FIECkUiEQNLwKRIFIo\nEIkaXgQiQaRQIBI1vAhEgkihQCRqeBGIBJFCgUjU8CIQCSKFApGo4UUgEkQKBSJRw4tAJIgU\nCkSihheBSBApFIhEDS8CkSBSKBCJGl4EIkGkUCASNbwIRIJIoUAkangRiASRQoFI1PAiEAki\nhZL1YKUsRS4gzIpkPB8QZgUyXggIszwZL9rhNFF/wAz0BAHjF+koy1HhEh2lk8tClNp/6kCi\nngKxTGLPc9TZLBMhpsaIhRXF/Yrs8qs30YkUn3BhhQmKKTocZzkyngwIswwZT6fpMEuS8awd\n9n6fyj8DORQ9QcD4WTrq3yWPPB1lk1S4KEQZUX+OTenUkyKWmVD3fJLl1SJKRF1MjRELy7CE\n/2Vkl1+9wa0dx+vCrZ0f3NppAZE4XhdE8gORtIBIHK8LIvmBSFpAJI7XBZH8QCQtIBLH64JI\nfiCSFhCJ43VBJD8QSQuIxPG6IJIfiKQFROJ4XRDJD0TSAiJxvC6I5AciaQGROF4XRPIDkbSA\nSByvCyL5gUhaQCSO1wWR/EAkLSASx+uCSH4gkhYQieN1QSQ/EEkLPZGe6NjPWOKu5d0bTqqt\nA0SihheBSK0t0sSyhYZIG9ceO755VUlpHSASNbwIRGptkTZtX7afjXYOG1+FFgzKLU+CSNTw\nIhCppUXa9+mMIdI++48v75Bb4+H4jwz+I+HCigmKVECY5cl4JiDMcmQ8m6XDLEPG83Z4yl2l\nfwZyKHqCHD1+no6yFBUu0FGWpMIlIcqI+gvELEQ91DKJPU+yglpEmaiLqTFiYTmW9r+M9mqu\nIxoiJXoPMUOk/hXmi3Xb5NZ4GGg3ODiDVc4chXoXAGYHGiJt3coskVaaLwyBpJbhK1Lw8HIU\nX5Fa9yvSod64JdIB+1Zup9zyPLxHooYXwXukFn6PdMfC7u7uzsv6xjqPMjbVdVhueR5EooYX\ngUgtLJJ1Bnr2TLFNq4+NrF9TVloHiEQNLwKRWlgkC+PWjqW29C7tG1dbB4hEDS8CkVpdJA0g\nEjW8CESCSKFAJGp4EYgEkUKBSNTwIhAJIoUCkajhRSASRAoFIlHDi0AkiBQKRKKGF4FIECkU\niEQNLwKRIFIoEIkaXgQiQaRQIBI1vAhEgkihQCRqeBGIBJFCgUjU8CIQCSKFApGo4UUgEkQK\nBSJRw4tAJIgUSnzchRXGKaYCwixLxhMBYZYm4yk6nGZJMp5NWM2YW7/XlaRnSNNRenxneDnK\npqhwno6yCSpcFKKMqD/HJtWZ1SqTLKXEiD2fYHm1iBJRF1NjxMIyTNiZyC6/ehOdSNm8Cyvn\nKQoBYVai43S4yIp0PCAckF6ywxm3/tBD6AkCkkt0lBXIMB1lVDQvbiIj6qdmIeqhKqf2nDo9\nxJksE+WWiULE4iK7/OoNbu04Xhdu7fzg1k4LiMTxuiCSH4ikBUTieF0QyQ9E0gIicbwuiOQH\nImkBkTheF0TyA5G0gEgcrwsi+YFIWkAkjtcFkfxAJC0gEsfrgkh+IJIWEInjdUEkPxBJC4jE\n8bogkh+IpAVE4nhdEMkPRNICInG8LojkByJpAZE4XhdE8gORtIBIHK8LIvmBSFpoiPTqxu4l\ntxxhLHHX8u4NJ9XWASJRw4tApBYWqbD87pHXt1yeZhvXHju+eVVJaR0gEjW8CERqYZEmH0oz\nNtIxPNo5bHwVWjAot26eb3cgEkQygUgS8fuuye+z/2bsDrnlORCJGl4EIrW0SKVPddx8ivWv\nMJ+v2ya3xsP+ToNDRRdWLlKU6HCV6SVWIuPlKtPtcM5dZegh9ARByXS0umQqKiUzon7qQGIK\nqnJqz6nTQ82gN6u0BRFeyvVF6yvSay9sujrRv9J8aggktcbDzy4y+EXZhbEySVA4IB5Addkh\n6d6HBtQ6QzXJASsNiOokM6J+3f3UTSPyqFBNadFdyXVG89vfpcW7D9i3cjvllqfg1o4aXgS3\ndi18a/f8VVnjX5Glu8c6jzI21XVYbnkeRKKGF4FILSxSYtntr57YtvAE27T62Mj6NWWldYBI\n1PAiEKmFRWKvfHHRZZ8bZCy1pXdp37jaOkAkangRiNTKImkCkajhRSASRAoFIlHDi0AkiBQK\nRKKGF4FIECkUiEQNLwKRIFIoEIkaXgQiQaRQIBI1vAhEgkihQCRqeBGIBJFCgUjU8CIQCSKF\nApGo4UUgEkQKBSJRw4tAJIgUCkSihheBSBApFIhEDS8CkSBSKBCJGl4EIkGkUCASNbwIRIJI\noWQ4p1lkVLIlImiEWZGM5wLCrEDG83S4wPJkvGiHU2r9mTw9Q4GO0uMX6SjLUeESHWVZMixE\nif03ZlEPLKj15InKiT3PMuK0lalyw2q1CxG3ILLLr95EJ1Ji0sEWaVIlXiCCRpjlyHiSDqdY\nhoyn6XCGpch4zg5PuPWHzpCho/T4OTrq7ZKfPB1lU1S4KEQZUX+exZWjsmo9aZZWYsSeT7G8\nWkSJqIupsYK6sCxL+l9GdvnVmxm4tbNFUr/O49YOt3a4tQsHIlHDi0AkiBQKRKKGF4FIECkU\niEQNLwKRIFIoEIkaXgQiQaRQIBI1vAhEgkihQCRqeBGIBJFCgUjU8CIQCSKFApGo4UUgEkQK\nBSJRw4tAJIgUCkSihheBSBApFIhEDS8CkSBSKBCJGl4EIkGkUCASNbwIRGplkcY291x2U4yx\nxF3LuzecVFsHiEQNLwKRWlmkz64dfv3OpRm2ce2x45tXlZTWASJRw4tApBYWKd73KmNvdLw8\n2jlsfBVaMCi3PA8iUcOLQKQWFsniSNf4PvuPL++QW+Ph5OMGI3EHW6S4SrJIBI0wy5PxdECY\nZcl4NiDM0mQ8Z4e9X9EMnYGeIGD8HB1lKSpcSJJRlqDCRSHKiPrzTB2PqCfDMkqM2PMEK6hF\nlIm6mBojFiZtQYSXcn3REyl+7f2sf4X5bN02uTUeBtoNDvJkW6ToS50RCvUuAMwOtER67er7\nyqx/pfnUEEhqjYf/+LbBsaSDLVJSJV0kgkaYFch4lg5nWI6M5wLCLEvG83Y44S7SNzE9Az1B\nwPh5OsrSVLhIR1mKCpeEKCPqLxAHEvVQyyT2PMWI01Ym6mJqjFhYnmX8L6O7kuuMjkiD3buM\nxwP2rdxOueVZeI9EDS9S3/dIp4lnBu+RokRDpBeX/MJsxjqPMjbVdVhueRpEooYXgUgtLFLu\nqh+YK86wTauPjaxfU1ZaB4hEDS8CkVpYpMEOi90staV3ad84U1oHiEQNLwKRWlgkXSASNbwI\nRIJIoUAkangRiASRQoFI1PAiEAkihQKRqOFFIBJECgUiUcOLQCSIFApEooYXgUgQKRSIRA0v\nApEgUigQiRpeBCJBpFAgEjW8CESCSKFAJGp4EYgEkUKBSNTwIhAJIoUCkajhRSASRAoFIlHD\ni0AkiBTK1CkH+3SdUpnIE0EjzLJkPJ6hwyxFxpMBYRYn4xkn7NbvnyFJHZGio/T4GTrKJqlw\njo6ycSpcGPO/YkT9WTZB1nOaeGYSxDKn1FMxznJqESWiLqbG8mohae9CMYns8qs30YmUKzjY\np6ugUiwTQSPMSnQ8IByQXgoIsyIdt8NZt/7QGegJAsYv0VE6uUxHGRUtiJvIiPrLxCxmPaeJ\nZ4ZaJrXnjDhtRGlUucTCpC2I7PKrN7i143hduLXzg1s7LSASx+uCSH4gkhYQieN1QSQ/EEkL\niMTxuiCSH4ikBUTieF2zXCTuE0SKEojE8bogkh+IpMWMiXSa6hNEgkgQKRSIRA0vApEgUigQ\niRpeBCJBpFAgEjW8CESCSKFAJGp4EYgEkUKBSNTwIhAJIoUCkajhRSASRAoFIlHDi0CklhZp\n5IYus0nctbx7w0m1dYBI1PAiEKmVRdrbu8USaePaY8c3ryoprQNEooYXgUitLNKTb+w3RRrt\nHDa+Ci0YlFueBpGo4UUgUiuLxJgl0j77b8bukFueBJGo4UUgEkRi/SvMp+u2ya3x8FyPwb/z\n3x4WTpfwe8z4VfNG+FVzfmbwq+ZRUoVIK82nhkBSazwMtBsc5NnC6Yq+4miZPWcynNOa6sw0\nGfoiHbBv5XbKLU/CrR01vEi9bu1OI8CtXaToizTWeZSxqa7DcsuTIBI1vAhEamWRxkf3dJnX\nwKbVx0bWrykrrQNEooYXgUitLNKVHSYPs9SW3qV940xpHSASNbwIRGplkTSBSNTwIhAJIoUC\nkajhRSASRAoFIlHDi0AkiBQKRKKGF4FIECmUSiI5LyASRIJIYbSaSM6a7PGVr78QyVoCRKqe\nlhFJvB4hEq8LIkUDRIJIyhIgUvW0qEjEav3Di0AkiBQKRIJIyhIgUvVAJIikLAEiVQ9EgkjK\nEiBS9UAkiKQsASJVTwuIVEEhiASRIiLNEU6XECqlKTKsSMZzBTrM8mQ8HxBmOTJesMNJtf6g\nGfL5UJGU4eVJWZYKF+koy1DhkhAl9p84kKyVWmZOPRXk6SkTdTE1RixM2oLILr96E51IybiD\ncLqEUDFOkWR5Mp4OCLMsGc8GhFmajOfs8KRbf+gM2WyoSMrw8qQsRYULdJQlqHBJiDKi/rx3\nLhzIWjMsowxO7HmCFdQiymqoyNRYQS5E2YLILr96g1s7jteFWzs/uLXTAiJxvK7aRfIWDZGs\nJUCk6oFIEElZAkSqHg2RhJgLRIJIzQ9E4nhdEMkPRNICInG8LlUkXYMqLdMEIkGkUCASRFKW\nAJGqByJBJGUJEKl6IFK4TxAJIoUCkSCSsgSIVD0QqQFFqlQXRIoSiMTxuvwiTUchiASRaqBa\nkdwXEAkiNT8QieN1RSuSf9EQCSKpJO5a3r3hpPsSImkAkSCSwsa1x45vXlXiL2eZSJH6o1zE\nfqiYCURqJmoXabRz2PiqtGCQv65RpOALrE4iTcMPbYJ3xQdEaiZqF2mf/ceYdxgPYwcNXp90\nEE4XESJj/h7elZukSLEMGc8EhFmKjGft8IS7HrL8NxGlwjybogovxv2vmFK/eaCbUu2sKXXP\np1heLaJM1MXUWCGhhLIsSdff5NQuUv8K83HdNuNhoN3gIO8QThQRImP+HmqECrGaV2BSUCKh\n196bAVWR5ooinhzoMQ2RVpqPlkjD9xj82veJFgGfclLdh59kZ/bDT+zP4Kjqw0+qGL9Afp5J\nvT78hPwwFmqZxJ7jw0+0qF2kA/at3U7+2nfrywJ+FbbZf0O2ivEb6/eRjHrUKvEeKUpqF2ms\n8yhjU12H+WuIRA0vApEgksqm1cdG1q8p85cQiRpeBCJBJJXUlt6lfePuS4hEDS8CkSBSKBCJ\nGl4EIkGkUCASNbwIRIJIoUAkangRiASRQoFI1PAiEAkiVUH+ouurSX/loq9Uk/5vF32/mvQf\nXvRUNekmT130Q/3kf77oGf3kr14U00++5aKJ8CSKL110XCftsYt+opM2cdEtWrNe/Vdaadsv\n+qVWXrMxIyK1X1VN+nD7hmrSn2nfXk36A+17qkk32dP+gH7yd9qf1E/+SvuL+smfbR8PT6JY\n1z6ik7a7Xevfi/H2z2rN2vOnWmn3ef+XbFYBkVQgkh+IpAVEUoFIfiCSFjMhUnHtP1aTfnLt\nzvAkjxfXDlSTvnftv1eTbvLva/fqJz+z9lf6yQ+vfU0/+dtrk+FJFD9YO6aT9su1+3XSkmu/\nrTXrPTdrpT229qhWXrMxEyIB0HJAJAAiACIBEAEzIJL06UJhXNdhsEgvd+SGripmcLKrmaCK\n0dnY5p7Lborp5z/RsV83+aefvuTvfl71TtroHMS3pFKutNdBqdImB6TJe1XTyhqaGRBJ+nSh\nMFbusn58rsPe3i1d+jPw7ComYPqjM/bZtcOv37k0o5s/sWzhfs3Bn+h97uRPrkpVu5M2Ogfx\nLamQK+91QKq8yQFp8l7VtLKGJnqR5E8XCuPS57SHfvKN/V36MzjZ1UzA9Edn8b5XGXuj42Xd\n/E3bl+3XHPyqJ6uqREDrIGdLKuVKex2UKm1yQJq8VzWtrLGJXiTfpwvpkO+45/or+rR+8GFg\nnTXtGazs6iaosv4jXeOa+fs+nTFE0ko+1fHkdZfecKTanXTm0TiIb0nlXGGvg1OFTa40om+v\nalpZYxO9SL5PF9Jhctndsdj6ZZo/MrHOmvYMVnZ1E1RXf/za+zXzE72HmCGSVnKs4+bX4tsu\nn6xyJ210DuJbUjlX2OvgVGGTK4zo36uaVtbYzIBI3qcLaZNepPnfD+yTqzuDfddR1QRV1f/a\n1feVNfO3bmWWSDrJsQ7jnqe45IladlK/fGNLKucKex2cKmxycJqwVzWtrLGJXiT504W0uPZB\nvTzrrGnP4J1j7QmqqX+we5du/qHeuCWSVvJoh/nD/1U7a9pJ/YOufbByrrDXwanCJgemiXtV\n08oam+hFkj9dKIRX7i0wllmk+d9+rLOmPYOVXd0EVYz+4pJfaOffsbC7u7vzsj6t5FKvcdHl\nLttb5U7a6BzEt6RyrrDXwanCJgelSXtV08oamxn49rf06UIhxLu3nBjpW5nVyR0f3dNl/nac\n3gxOdjUTWGjWn7vqB+ZvpulVY/1aXc+eKb3Bdy49NHpPr+46JTQOcrekQq681wGp8ibTacpe\n1bSyhmYGRJI+XSiM4VsX92z8jVbqleZP/Toe1pyBZ1cxgYVm/YPW8B279ddr3NrpJZe+s+yS\nm16teidtdA7iW1IhV97rgFR5k+k0Za9qWllDg/8iBEAEQCQAIgAiARABEAmACIBIAEQARAIg\nAiASABEAkQCIAIgEQARAJAAiACI1P/PfXe8KAESaBUCkBgAiNT8QqQGASM3F/D8omM2f/n9F\n9oMPvuWt7T9gtkjve58Z7voD4+Hpv3zrW86v6vPRwfSBSM3F1+eYvzP/H7+1hv3znEt27/7Y\nnN2ySE/8zl/s2vOZOXfWudBWAyI1F6O/e7XxuHnOIdZ3cY6xqd9dKot0/rtSxrPOt2bqW2ir\nAZGajI//PyXGPvhe/vKMP5dEOjnn+ozBP875ef1qbEUgUpPxvTlPsf8553bji9Ft5/0fv/M7\nc+ZLIh2a4/BQvSttLSBSk5H4/VXsK7/1KmN/8Tu37H3hV22qSFfst5g9f+e4KYBIzcbiNvaB\nCxk7Osf8s4iF/+yIdP55Zt+f/gEbm7O8vvW1KBCp2Xh4zo/nbGdsaI75B0PvmfMhW6SL/7DM\n2Mm3/AFjf/J/mn8M/TvrCvUutLWASM1G/v8+8z9PGc3c0x9+9oYLL3zrQNIUaeucTb95/qL3\nGiI9/Xt/9J3Hbv29FfWus8WASE3H1XOsP/b03J/9/v/7t1O7/vD/ipki5dac/p/et2vVW42O\nn/3VW3/v7DvwBenNBSIBEAEQCYAIgEgARABEAiACIBIAEQCRAIgAiARABEAkACIAIgEQARAJ\ngAiASABEAEQCIAIgEgARAJEAiID/BfL+r+rtRMoPAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bring data locally\n",
    "df <- to.casDataFrame(castbl, obs = nrow(castbl))\n",
    "# Use reshape2's melt to help with data formatting\n",
    "d <- melt(df[sapply(df, is.numeric)], id.vars=NULL)\n",
    "ggplot(d, aes(x = value)) +\n",
    "    facet_wrap(~variable,scales = 'free_x') +\n",
    "    geom_histogram(fill = 'blue', bins = 25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From our plots above, we can see that most of our home equity loans are _not bad_, meaning that they must be good. We can also see that most of our variables have a slight right skew. Let’s keep exploring our data by checking the missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 13 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Column</th><th scope=col>NMiss</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>BAD    </td><td>   0</td></tr>\n",
       "\t<tr><td>LOAN   </td><td>   0</td></tr>\n",
       "\t<tr><td>MORTDUE</td><td> 518</td></tr>\n",
       "\t<tr><td>VALUE  </td><td> 112</td></tr>\n",
       "\t<tr><td>REASON </td><td> 252</td></tr>\n",
       "\t<tr><td>JOB    </td><td> 279</td></tr>\n",
       "\t<tr><td>YOJ    </td><td> 515</td></tr>\n",
       "\t<tr><td>DEROG  </td><td> 708</td></tr>\n",
       "\t<tr><td>DELINQ </td><td> 580</td></tr>\n",
       "\t<tr><td>CLAGE  </td><td> 308</td></tr>\n",
       "\t<tr><td>NINQ   </td><td> 510</td></tr>\n",
       "\t<tr><td>CLNO   </td><td> 222</td></tr>\n",
       "\t<tr><td>DEBTINC</td><td>1267</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 13 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " Column & NMiss\\\\\n",
       " <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t BAD     &    0\\\\\n",
       "\t LOAN    &    0\\\\\n",
       "\t MORTDUE &  518\\\\\n",
       "\t VALUE   &  112\\\\\n",
       "\t REASON  &  252\\\\\n",
       "\t JOB     &  279\\\\\n",
       "\t YOJ     &  515\\\\\n",
       "\t DEROG   &  708\\\\\n",
       "\t DELINQ  &  580\\\\\n",
       "\t CLAGE   &  308\\\\\n",
       "\t NINQ    &  510\\\\\n",
       "\t CLNO    &  222\\\\\n",
       "\t DEBTINC & 1267\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 13 × 2\n",
       "\n",
       "| Column &lt;chr&gt; | NMiss &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| BAD     |    0 |\n",
       "| LOAN    |    0 |\n",
       "| MORTDUE |  518 |\n",
       "| VALUE   |  112 |\n",
       "| REASON  |  252 |\n",
       "| JOB     |  279 |\n",
       "| YOJ     |  515 |\n",
       "| DEROG   |  708 |\n",
       "| DELINQ  |  580 |\n",
       "| CLAGE   |  308 |\n",
       "| NINQ    |  510 |\n",
       "| CLNO    |  222 |\n",
       "| DEBTINC | 1267 |\n",
       "\n"
      ],
      "text/plain": [
       "   Column  NMiss\n",
       "1  BAD        0 \n",
       "2  LOAN       0 \n",
       "3  MORTDUE  518 \n",
       "4  VALUE    112 \n",
       "5  REASON   252 \n",
       "6  JOB      279 \n",
       "7  YOJ      515 \n",
       "8  DEROG    708 \n",
       "9  DELINQ   580 \n",
       "10 CLAGE    308 \n",
       "11 NINQ     510 \n",
       "12 CLNO     222 \n",
       "13 DEBTINC 1267 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the number of missing values for all variables\n",
    "tbl <- cas.simple.distinct(castbl)$Distinct[,c('Column', 'NMiss')]\n",
    "tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>BAD</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>LOAN</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>MORTDUE</dt>\n",
       "\t\t<dd>518</dd>\n",
       "\t<dt>VALUE</dt>\n",
       "\t\t<dd>112</dd>\n",
       "\t<dt>YOJ</dt>\n",
       "\t\t<dd>515</dd>\n",
       "\t<dt>DEROG</dt>\n",
       "\t\t<dd>708</dd>\n",
       "\t<dt>DELINQ</dt>\n",
       "\t\t<dd>580</dd>\n",
       "\t<dt>CLAGE</dt>\n",
       "\t\t<dd>308</dd>\n",
       "\t<dt>NINQ</dt>\n",
       "\t\t<dd>510</dd>\n",
       "\t<dt>CLNO</dt>\n",
       "\t\t<dd>222</dd>\n",
       "\t<dt>DEBTINC</dt>\n",
       "\t\t<dd>1267</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[BAD] 0\n",
       "\\item[LOAN] 0\n",
       "\\item[MORTDUE] 518\n",
       "\\item[VALUE] 112\n",
       "\\item[YOJ] 515\n",
       "\\item[DEROG] 708\n",
       "\\item[DELINQ] 580\n",
       "\\item[CLAGE] 308\n",
       "\\item[NINQ] 510\n",
       "\\item[CLNO] 222\n",
       "\\item[DEBTINC] 1267\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "BAD\n",
       ":   0LOAN\n",
       ":   0MORTDUE\n",
       ":   518VALUE\n",
       ":   112YOJ\n",
       ":   515DEROG\n",
       ":   708DELINQ\n",
       ":   580CLAGE\n",
       ":   308NINQ\n",
       ":   510CLNO\n",
       ":   222DEBTINC\n",
       ":   1267\n",
       "\n"
      ],
      "text/plain": [
       "    BAD    LOAN MORTDUE   VALUE     YOJ   DEROG  DELINQ   CLAGE    NINQ    CLNO \n",
       "      0       0     518     112     515     708     580     308     510     222 \n",
       "DEBTINC \n",
       "   1267 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Easy way to get missing values for numeric variables\n",
    "cas.nmiss(castbl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RApaUAiiBSASIiESAqIZEGkpAGJIFIAIiESIikgkgWRkgYkgkgBiIRIiKSASBZE\nShqQCCIFIBIiIZICIlkQKWlAIogUgEiIhEgKiGRBpKQBiSBSACIhEiIpIJIFkYYuIAlEQqTw\nnAxFZaECksilSIkuE5EQCZH6SHSZiIRIiNRHostEJETKZUASiJR1DCK5nBP3ypIEJIFIWccg\nkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccgkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccg\nkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccgkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccg\nkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccgkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccg\nkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccgkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccg\nkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccgkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccg\nkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccgkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccg\nkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccgkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccg\nkss5ca8sSUASiJR1DCK5nBP3ypIEJIFIWccgkss5ca8sSUAS5RCpW6PH9KjHpF3Zqy3LJcCh\nqyxJQFlcplKZZLFd6Yr0ssYWs109JprtZotj5S6zMf4AlwCHrlIOaPNwCkjCfYJ2aAElWmy6\nIqkvr7y1461dssssx1s79WIQCZGSXSYiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2DSC7nxL2yJAFJIFLWMYjkck7cK0sSkAQiZR2D\nSC7nxL2yJAFJDAORti6cOvGS9Y3b57ZXGZ+iSOknn885ca+UQCSF3Ih06ZxVL1w1o6dhe9qP\nq+fYgEiDWCmBSAp5EanSsbL6SjT6sYbtcQ83HqOBSIiU7DKLL9Lysb3V25k3h7e72v/zvNMX\nrEGkQayUQCSFvIi09DT/dl4YSaEAABXcSURBVO7i8PamU//jqafmn7qteuee66677rvbNDrN\nrvgDXC5G6dlttmfVcygqJXaZTiWIQgUkoU6QSJcWUJLFbt0DkaaFRAptmx3jl/l32traTtTO\noeNyMUPXcygqhwAuU11sd3AWVaQH+t7OLWneNuacm6o3v73zzjvv2aKxw+yMP8DlYpSeu83W\nrHoORaXETrNDCaJQAUmoEySySwsoyWI3ty7Sho6njdk86vHw9nNf2m1M5/i7+49R36fyGYnP\nSMkus/ifkczl569aM392r1l2W7C9ZeKiF9csmLYTkQavUgKRFHIj0vZFUyYt2GjMlfPq2yvn\nTZh86brgEPViEAmRkl3mMBCpBdSLQSRESnaZiNRaDOknn885ca+UQCQFREqWfD7nxL1SApEU\nEClZ8vmcE/dKCURSQKRkyedzTtwrJRBJAZGSJZ/POXGvlEAkBURKlnw+58S50r1lsQKSQKTW\nYkg/+XzOiXOle8tiBSSBSK3FkH7y+ZwT50r3lsUKSAKRWosh/eTzOSfOle4tixWQBCK1FkP6\nyedzTpwr3VsWKyAJRGothvSTz+ecOFe6tyxWQBKI1FoM6SefzzlxrnRvWayAJBCptRjSTz6f\nc+Jc6d6yWAFJIFJrMaSffD7nxLnSvWWxApJApNZiSD/5fM6Jc6V7y2IFJIFIrcWQfvL5nBPn\nSveWxQpIApFaiyH95PM5J86V7i2LFZAEIrUWQ/rJ53NOnCvdWxYrIAlEai2G9JPP55w4V7q3\nLFZAEojUWgzpJ5/POXGudG9ZkoBEEClZfsNsTtxbliQgEURKlt8wmxP3liUJSASRkuU3zObE\nvWVJAhJBpGT5DbM5cW9ZkoBEEClZfsNsTtxbliQgEURKlt8wmxP3liUJSASRkuU3zObEvWVJ\nAhJBpGT5DbM5cW9ZkoBEEClZfsNsTtxbliQgEURKlt8wmxP3liUJSASRkuU3zObEvWVJAhJB\npGT5DbM5cW9ZkoBEEClZfsNsTtxbliQgEURKlt8wmxP3liUJSASRkuU3zObEvWVJAhJBpNYu\npiSV7i1FECmFlohUsEr3liKIlEJLRCpYpXtLEURKoSUiFazSvaUIIqXQEpEKVuneUgSRUmiJ\nSAWrdG8pgkgptESkglW6txRBpBRaIlLBKt1biiBSCi0RqWCV7i1FECmFlohUsEr3liKIlEJL\nRCpYpXtLEURKoSUiFazSvaUIIqXQEpEKVuneUgSRUmiJSAWrdG8pgkgptESkglW6txRBpBRa\nIlLBKt1biiBSCi0RqWCV7i1FECmFlohUsEr3liKIlEJLRCpYpXtLEURKoSUiFazSvaUIIqXQ\nEpEKVuneUgSRUmiJSAWrdG8pgkgptESkglW6txRBpBRaIlLBKt1biiBSCi0RqWCV7i1FECmF\nlohUsEr3liKIlEJLRCpYpXtLEURKoSUiFazSvaUIIqXQEpEKVuneUgSRUmiJSAWrdG8pgkgp\ntESkglW6txRBpBRaIlLBKt1biiBSCi0RqWCV7i1FECmFlohUsEr3liKIlEJLRCpYpXtLEURK\noSUiFazSvaUIIqXQEpEKVuneUgSRUmiJSAWrdG8pgkgptExXpI0aW82O+ANcLqZUle4tRbrM\nK8PpSZHoNFuzusyNGzekK1KXxm7THX+Ay8WUqtK9pUiP9ry59xyKSvkyd2d1mV1dO9MVSXkP\nwVu75JXuLUV4a5dCS0QqWKV7SxFESqElIhWs0r2lCCKl0BKRClbp3lIEkVJoiUgFq3RvKYJI\nKbREpIJVurcUQaQUWiJSwSrdW4ogUgotEalgle4tRRAphZaIVLBK95YiiJRCS0QqWKV7SxFE\nSqElIhWs0r2lCCKl0BKRClbp3lIEkVJoiUgFq3RvKYJIKbREpIJVurcUQaQUWiJSwSrdW4og\nUgotEalgle4tRRAphZaIVLBK95YiiJRCS0QqWKV7SxFESqElIhWs0r2lCCKl0BKRClbp3lJk\neIk0BC19EKlgle4tRRAp6WIriFS4SveWIoiUdLEVRCpcpXtLEURKutgKIhWu0r2lCCIlXWwF\nkQpX6d5SBJGSLraCSIWrdG8pgkhJF1tBpMJVurcUQaSki60gUuEq3VuKIFLSxVYQqXCV7i1F\nECnpYiuIVLhK95YiiJR0sRVEKlyle0sRREq62AoiFa7SvaUIIiVdbAWRClfp3lIEkZIutoJI\nhat0bymCSEkXW0GkwlW6txRBpKSLrSBS4SrdW4ogUtLFVhCpcJXuLUUQKeliK4hUuEr3liKI\nlHSxFUQqXKV7SxFESrrYCiIVrtK9pQgiJV1sBZEKV+neUgSRki62gkiFq3RvKYJISRdbQaTC\nVbq3FEGkpIutIFLhKt1biiBS0sVWEKlwle4tRRAp6WIriFS4SveWIoiUdLEVRCpcpXtLEURK\nutgKIhWu0r2lCCIlXWwFkQpX6d5SBJGSLraCSIWrdG8pgkhJF1tBpMJVurcUQaSki60gUuEq\n3VuKIFLSxVYQqXCV7i1FECnpYiuIVLhK95YiiJR0sRVEKlyle0sRREq62AoiFa7SvaUIIiVd\nbAWRClfp3lIEkZIutoJIhat0bymCSEkXW0GkwlW6txRBpKSLrSBS4SrdW4ogUtLFVhCpcJXu\nLUUQKeliK4hUuEr3liKIlHSxFUQqXKV7SxFESrrYCiIVrtK9pQgiJV1sBZEKV+neUgSRki62\ngkiFq3RvKYJISRdbQaTCVbq3FEGkpIutIFLhKt1biiBS0sVWEKlwle4tRRAp6WIriFS4SveW\nIoiUdLEVRCpcpXtLEURKutgKIhWu0r2lCCIlXWwFkQpX6d5SBJGSLraCSIWrdG8pgkhJF1vZ\nI5G2Lpw68ZL1jdvhfYg0GJXuLUUQKeliK3sk0qVzVr1w1Yyehu3wPkQajEr3liKIlHSxlT0R\nqdKxsvoKNPqx8HZ4HyINSqV7SxFESrrYyp6ItHxsb/V25s3h7fA+RBqUSveWIoiUdLGVPRFp\n6Wn+7dzF4e3QvmvOPvvsj3Vp7Dbd8Qe4XEypKt1bivQY5QD3nkNQOQQtq+zcA5GmhUSqbYf2\nzW1raztROwfA8KQ72FJFeqDvbdyS8HZ4n4/yHqKFt3Yi281mx0r1nYtIT49j4Qazy7GSgBTy\nGlDrIm3oeNqYzaMeD2+H9yFSneE3J9EQUEDrIpnLz1+1Zv7sXrPstvp2/1dEamD4zUk0BBSw\nByJtXzRl0oKNxlw5r77d/xWRGhh+cxINAQXsgUgtoLbLawzRMCcKBBSASDEwJwoEFIBIMTAn\nCgQUgEgxMCcKBBSASDEwJwoEFIBIMTAnCgQUgEgxMCcKBBSASDEwJwoEFIBIMTAnCgQUgEgx\nMCcKBBSASDEwJwoEFIBIMTAnCgQUgEgxMCcKBBSASDEwJwoEFIBIMTAnCgQUgEgxMCcKBBSA\nSDEwJwoEFIBIMTAnCgQUgEgxMCcKBBSASDEwJwoEFIBIMTAnCgQUgEgxMCcKBBSASDEwJwoE\nFIBIMTAnCgQUgEgxMCcKBBSASDEwJwoEFIBIMTAnCgQUgEgxMCcKBBSASDEwJwoEFJCuSCor\njr/esfJbxz/gWDn3+FccK8d/2LFww/GfcqxcfvwNjpXfOv5Bx8pPOgc0boJj4cvOAd1//I2O\nldc5B/SJ4ze1fvDgiPTLtm84Vi5uu8+xclbbBsfKD/4fx8JK24WOlfe2fdOx8utt9ztWzmrb\nqB8UyQc+5Fj4UtvHHCvvabvasdI9oPPb9uBfGkQaCCIpINJAEGkgiKSASAMZHJGenHOXY+XP\n5/zesfJbc7Y6Vn52gWPh5jmuHwWTBPSUY+V1c7Y5Vn72csdC94CecA7oTueArp2zvfWDB0ck\ngGEOIgGkACIBpEBmIs1qb2+fMPsXdnvTmGk9tX0dE+fd3RtZUPnK9DGnXvq4f9jX+vf1F5rK\nVz8yZtKcu2vnrXKGXj5r7Av+lxk/qd68VH30lE89VF9b/zrqpwsWfHnfrvZFZuHFjWep9Ygu\nr3Vtrunr/LDWuX3SxfatfGih9WsewLZqwW/CB4fTjiYINRxF8MTUFzyrfaW/2XNqe3dzg741\n1uMJXcz0m+z5TlvS+ASFzhufi2lo0XCO/jX2LDl3/JizlvQ2XEL4jPMutafpnnJj6MJqVy2s\nbwD3jnnO/3LXyasbgqrlHUd2In2hUvnjte1P+9vfmzf5gdq+9Y/fPOGKKJOenzRj+erfLBx1\nf1ik/sLVE2cuX/37G8dUP6rOWrjW5yW9fNbET/pf/JCfO+Xc5auf+HLHd4O19a+jfrpgwRvX\nrl3R/ujatZv6nvz6Wfp7nBFZHhapXvNHf+VP/FfHEqVz5Q+fG7+uYaGhax5A79rfVZ/Y0MHh\ntIXno7a+hiiCJ6a+4FmTF/ubD0+sitTUoG+N9XhCFxMe1NATFDpvfC61bPpjCJ+jf43fnPrw\nxo3/PeHGhksIndEsH/Wyv71iVCV0YREiRQ9QjQUXVK9m08RbGoPqyzuW7ETyL6G7w/9eS+/0\nOxfPD/aZp0f9IuL4T56zy//yrZtCIgWFF5272/+y4qbekGVa+aybJ95p+kK+YJatv7Xjufra\n+tZRP11owebp9uf9L/bJr5+lv8dUsXxgzcfOs52/3/FHpbPpHn97w0JD1xxBpfrEhg4OLz6a\n/nbhKOpPTH3BsxZN8g+44oqqSM0N+tYYxBO6mPCghp6g0Hnjc2mKIXSOYI0zv+XfPvpIwyWE\nzmh6TrP/OFxy2YCJE9c3kFcm/tCYqy7oaZ6ZypCK1HXbKf7Pch4a37myY70JruHS+QMP39T+\n84ZSS3/hxvZfDHxQLZ91+52nbLIhv9i+wu7pnnR9+Ah/HY3jXFtwg0jBWYIeYvnAmvXty/s6\nT7xBK+2d8MPwQsPXHEH1iQ1fVXjx0dTaNURRf2KCBVc3z6gueeu4+9q7mxv0rdE0iWQvRhKp\nft74XBpjCJ8jWOPCj9ZebxuWVW9gzE3Tq//mVDoeGThxrYtkfjFu3SP+G7ummRlCkUaPH98x\nyf6a0/wvGnOev5zaNdxw5sDDnwq9LQkutb/wib737cF5q9yhl8+63cy90ob8SPvavl0XXRE+\nwl9H/XShBTeK1H+WoEd0eYNI/TWPttTZ37njmjHrwgsNX3ME1Sc2fFXhxUdT69wQRf2JCRZc\n3fzeZ4z5ycW/b+9uaBCs0TSL5F9MeFBDT1DovPG5BKfraxE6R7DGLVeO+sjCpZsaLyHUoPrv\n7ehfVW06o3fgxEnri+LST06/pTmoIRWp+lb02aWn/NSYdR1PGHP7qd3BZV139sDDn2qv/+C1\n/+qDwiftCE8YNWrUg/1vcbfr5dWQ1459uO9pe75v18evDB/hr6N+uvqCm0WqnSXoEV3eKFKt\n5pH2NX27G0VqKvWf2vZzHzXhhYavOYLqExu+qvDio6l1DheFnphgwdXNDSdvNBfcVxWpoUGw\nRtMskn8xAz+DbG98CpRc7MH1FvVz1NdYVWn5NeecfHfDJYQaVLl8gek9/QcREyetL4oNE2b1\nNAVlhvqtXfVD36nVoNsnTJgwvv2+4LIuivjNga0dfVn09NavPijc0vGz6t3Vzz334RXCK3NU\neTVks+T0zpk/qaZwr93TPfGm0NrsOprfYPkLbhapdpagR0x5c8269l/21Uz+dkzn6lP7zCn+\nP5GhhYavOYJK+2/DVxVefDS1duGi0BMTLNjf/Mz3n5+4uypSQ4NgjaZZJP9izrS/AtY77rbm\nt3bBeeNzaYwhdI76Gvv4xvjGZdUbVPmf0Rt/dfKWiImT1hed1OLmoOzd38ZXZSzSd8ea3ZNv\nWl/linn9+1a0R43GxafbfyOunxtcaqhw/hmd/p5eUaSIchty98zF51VDvmhml7/n9lEvhNZm\n19FsQnXBZoBItbP095gglw+omX2e/cd06Zj1SudfjK2+MQ8vNHTNTfzgWmNWVlcYOji8+Gj6\n29WLwk9MfcHVzRXn3bDYVEUa0KBvjU0i2Yu5bJb/LZHftP9uoEi188bn0hRDcI76Gl+6wn6f\n7b6OzoZl1c/oc/YPPvcfZuDEieuLTmpxU1D9eceS6be/X1w+6avmv8ds9u8/3vGC3ffk9aO/\nGHX8C5M/cs/q3y4a+2j/i+/GUOG6KR/95Zpn77pg6tr+V+a13Vq5Ddk8NXqy/43riec+uObp\nqztura+tto766YIFmwEi1c7S3+PMyPJa16aapz88+zcbVl8/+odxne1Tu+DcroaFhq65iV+M\nvuu5S87uCR8cXrzwfNTWVy8KPzH1BVc3u6dMX2VFCjeor7EuUv1iVo9f+PvVP5+ysOG6GoKI\nz8Wert4iOEd9jT3nnvvg+pcePOPihpBCZ/S57dzxT5qGC6tdtbC+6KT6RKp36c87lkx/IDvm\nozd1mzl9n0vM2VfbfeMu/Hl0QeW/po2ZcsXKWml7++WhQrPx62eMmTDru9v7H2wf8A/EgPK+\nkM3idvvjv/86ffQpF/86tLbaOuqnCxZsBopUO0uth1jud22uWfO5ie1jL3sktrOdoE32Bzih\nhdavuZkl08Z/ak3DweHFy8+Hv756UfiJqS/Y37z2fGNFCjcIrTEQKfR0PrtgyrgZt3aZxico\ndN74XExji+AcoTVuufrMcWPOum5H43WHzlhl+7j/Z0zzxNmrjl5fdFJ9IoW69OcdB78ilD1f\njfjmCgyzXBApe54bfcOmHUO9iBwyrHJBpEHg3uknLx3qNeSR4ZQLIgGkACIBpAAiAaQAIgGk\nACIBpAAiAaQAIgGkACIVgXVzjh4x4qjz/tCw8z1vHqLVQASIVADue80+HZ/97IT9Rtwe3otI\neQKR8s+6w/7U/sd9T77+0HWh3YiUJxAp/3zC6/vvEc1dC18y5ifHjTjwLQt7+0Q65hh//6g/\nNea49977jgNfd2XXnNeNOGGlf/fR9488/N/Xx50XUgSR8s/fvSb0n0fcutcHfvjz2d7HmkU6\n4Q3HP/L8GO/ES9bc86oPVe8e+Y4719+yz9QhWnL5QKTc07vP8aF7R/2F/zfBRu/3crNI3mPG\n/NJ7d/XupEP8u/5/nX3C64ZiwaUEkXLPNm9U/c4L3ln+l6u925tFqtpjnvH8/6/Mhd4Wc8LB\n/gNT9x785ZYURMo9vfseV7/zkGf/MO9PvMXNIr2xuvGs5/8PV+Z4r/TdNdN5egcLks4/bx1R\n/8/fHvYu8b/c4X0TkXIFSeef+d6ivo3lb17xovdRf2uxt9SK9A9H+/fehUhDDknnn5ePOMj+\nJPax1x2+wRz9Ov+vdH3g4M1WpPcf1mvM+oMQacgh6QLw0OHeCfMvHbvPnz9afVO390k/+unZ\nvjC+SF/wLl/36PFvQaQhh6SLwMsXHX3IyGM+VfG3l733kAP+4RrTJ9Ku2a8/4JgfzxiJSEMN\nSQOkACIBpAAiAaQAIgGkACIBpAAiAaQAIgGkACIBpAAiAaQAIgGkACIBpAAiAaQAIgGkACIB\npMD/B3s6nZz3fwGwAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the missing data\n",
    "tbl$PctMiss <- tbl$NMiss/nrow(castbl)\n",
    "ggplot(tbl, aes(Column, PctMiss)) +\n",
    "    geom_col(fill = 'blue') +\n",
    "    ggtitle('Pct Missing Values') +\n",
    "    theme(plot.title = element_text(hjust = 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using both R and SWAT, we have explored our data, and now we have an idea of what to look for when we clean our data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we should impute those missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$ImputeInfo</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 12 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Variable</th><th scope=col>ImputeTech</th><th scope=col>ResultVar</th><th scope=col>N</th><th scope=col>NMiss</th><th scope=col>ImputedValueContinuous</th><th scope=col>ImputedValueNominal</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>LOAN   </td><td>Median</td><td>IMP_LOAN   </td><td>5960</td><td>   0</td><td>16300.00000</td><td>       </td></tr>\n",
       "\t<tr><td>MORTDUE</td><td>Median</td><td>IMP_MORTDUE</td><td>5442</td><td> 518</td><td>65019.00000</td><td>       </td></tr>\n",
       "\t<tr><td>VALUE  </td><td>Median</td><td>IMP_VALUE  </td><td>5848</td><td> 112</td><td>89235.50000</td><td>       </td></tr>\n",
       "\t<tr><td>REASON </td><td>Mode  </td><td>IMP_REASON </td><td>5708</td><td> 252</td><td>        NaN</td><td>DebtCon</td></tr>\n",
       "\t<tr><td>JOB    </td><td>Mode  </td><td>IMP_JOB    </td><td>5681</td><td> 279</td><td>        NaN</td><td>Other  </td></tr>\n",
       "\t<tr><td>YOJ    </td><td>Median</td><td>IMP_YOJ    </td><td>5445</td><td> 515</td><td>    7.00000</td><td>       </td></tr>\n",
       "\t<tr><td>DEROG  </td><td>Median</td><td>IMP_DEROG  </td><td>5252</td><td> 708</td><td>    0.00000</td><td>       </td></tr>\n",
       "\t<tr><td>DELINQ </td><td>Median</td><td>IMP_DELINQ </td><td>5380</td><td> 580</td><td>    0.00000</td><td>       </td></tr>\n",
       "\t<tr><td>CLAGE  </td><td>Median</td><td>IMP_CLAGE  </td><td>5652</td><td> 308</td><td>  173.46667</td><td>       </td></tr>\n",
       "\t<tr><td>NINQ   </td><td>Median</td><td>IMP_NINQ   </td><td>5450</td><td> 510</td><td>    1.00000</td><td>       </td></tr>\n",
       "\t<tr><td>CLNO   </td><td>Median</td><td>IMP_CLNO   </td><td>5738</td><td> 222</td><td>   20.00000</td><td>       </td></tr>\n",
       "\t<tr><td>DEBTINC</td><td>Median</td><td>IMP_DEBTINC</td><td>4693</td><td>1267</td><td>   34.81826</td><td>       </td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$OutputCasTables</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 1 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>casLib</th><th scope=col>Name</th><th scope=col>Rows</th><th scope=col>Columns</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>CASUSER(sasdemo05)</td><td>hmeq</td><td>5960</td><td>25</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$ImputeInfo] A casDataFrame: 12 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       " Variable & ImputeTech & ResultVar & N & NMiss & ImputedValueContinuous & ImputedValueNominal\\\\\n",
       " <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t LOAN    & Median & IMP\\_LOAN    & 5960 &    0 & 16300.00000 &        \\\\\n",
       "\t MORTDUE & Median & IMP\\_MORTDUE & 5442 &  518 & 65019.00000 &        \\\\\n",
       "\t VALUE   & Median & IMP\\_VALUE   & 5848 &  112 & 89235.50000 &        \\\\\n",
       "\t REASON  & Mode   & IMP\\_REASON  & 5708 &  252 &         NaN & DebtCon\\\\\n",
       "\t JOB     & Mode   & IMP\\_JOB     & 5681 &  279 &         NaN & Other  \\\\\n",
       "\t YOJ     & Median & IMP\\_YOJ     & 5445 &  515 &     7.00000 &        \\\\\n",
       "\t DEROG   & Median & IMP\\_DEROG   & 5252 &  708 &     0.00000 &        \\\\\n",
       "\t DELINQ  & Median & IMP\\_DELINQ  & 5380 &  580 &     0.00000 &        \\\\\n",
       "\t CLAGE   & Median & IMP\\_CLAGE   & 5652 &  308 &   173.46667 &        \\\\\n",
       "\t NINQ    & Median & IMP\\_NINQ    & 5450 &  510 &     1.00000 &        \\\\\n",
       "\t CLNO    & Median & IMP\\_CLNO    & 5738 &  222 &    20.00000 &        \\\\\n",
       "\t DEBTINC & Median & IMP\\_DEBTINC & 4693 & 1267 &    34.81826 &        \\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$OutputCasTables] A casDataFrame: 1 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " casLib & Name & Rows & Columns\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t CASUSER(sasdemo05) & hmeq & 5960 & 25\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$ImputeInfo\n",
       ":   \n",
       "A casDataFrame: 12 × 7\n",
       "\n",
       "| Variable &lt;chr&gt; | ImputeTech &lt;chr&gt; | ResultVar &lt;chr&gt; | N &lt;dbl&gt; | NMiss &lt;dbl&gt; | ImputedValueContinuous &lt;dbl&gt; | ImputedValueNominal &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| LOAN    | Median | IMP_LOAN    | 5960 |    0 | 16300.00000 | <!----> |\n",
       "| MORTDUE | Median | IMP_MORTDUE | 5442 |  518 | 65019.00000 | <!----> |\n",
       "| VALUE   | Median | IMP_VALUE   | 5848 |  112 | 89235.50000 | <!----> |\n",
       "| REASON  | Mode   | IMP_REASON  | 5708 |  252 |         NaN | DebtCon |\n",
       "| JOB     | Mode   | IMP_JOB     | 5681 |  279 |         NaN | Other   |\n",
       "| YOJ     | Median | IMP_YOJ     | 5445 |  515 |     7.00000 | <!----> |\n",
       "| DEROG   | Median | IMP_DEROG   | 5252 |  708 |     0.00000 | <!----> |\n",
       "| DELINQ  | Median | IMP_DELINQ  | 5380 |  580 |     0.00000 | <!----> |\n",
       "| CLAGE   | Median | IMP_CLAGE   | 5652 |  308 |   173.46667 | <!----> |\n",
       "| NINQ    | Median | IMP_NINQ    | 5450 |  510 |     1.00000 | <!----> |\n",
       "| CLNO    | Median | IMP_CLNO    | 5738 |  222 |    20.00000 | <!----> |\n",
       "| DEBTINC | Median | IMP_DEBTINC | 4693 | 1267 |    34.81826 | <!----> |\n",
       "\n",
       "\n",
       "$OutputCasTables\n",
       ":   \n",
       "A casDataFrame: 1 × 4\n",
       "\n",
       "| casLib &lt;chr&gt; | Name &lt;chr&gt; | Rows &lt;dbl&gt; | Columns &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| CASUSER(sasdemo05) | hmeq | 5960 | 25 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$ImputeInfo\n",
       "   Variable ImputeTech   ResultVar    N NMiss ImputedValueContinuous\n",
       "1      LOAN     Median    IMP_LOAN 5960     0            16300.00000\n",
       "2   MORTDUE     Median IMP_MORTDUE 5442   518            65019.00000\n",
       "3     VALUE     Median   IMP_VALUE 5848   112            89235.50000\n",
       "4    REASON       Mode  IMP_REASON 5708   252                    NaN\n",
       "5       JOB       Mode     IMP_JOB 5681   279                    NaN\n",
       "6       YOJ     Median     IMP_YOJ 5445   515                7.00000\n",
       "7     DEROG     Median   IMP_DEROG 5252   708                0.00000\n",
       "8    DELINQ     Median  IMP_DELINQ 5380   580                0.00000\n",
       "9     CLAGE     Median   IMP_CLAGE 5652   308              173.46667\n",
       "10     NINQ     Median    IMP_NINQ 5450   510                1.00000\n",
       "11     CLNO     Median    IMP_CLNO 5738   222               20.00000\n",
       "12  DEBTINC     Median IMP_DEBTINC 4693  1267               34.81826\n",
       "   ImputedValueNominal\n",
       "1                     \n",
       "2                     \n",
       "3                     \n",
       "4              DebtCon\n",
       "5                Other\n",
       "6                     \n",
       "7                     \n",
       "8                     \n",
       "9                     \n",
       "10                    \n",
       "11                    \n",
       "12                    \n",
       "\n",
       "$OutputCasTables\n",
       "              casLib Name Rows Columns\n",
       "1 CASUSER(sasdemo05) hmeq 5960      25\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Impute missing values\n",
    "cas.dataPreprocess.impute(castbl,\n",
    "    methodContinuous = 'MEDIAN',\n",
    "    methodNominal = 'MODE',\n",
    "    inputs = colnames(castbl)[-1],\n",
    "    copyAllVars = TRUE,\n",
    "    casOut = list(name = 'hmeq', \n",
    "                replace = TRUE)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we should partition our data into training and testing sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$outputSize</dt>\n",
       "\t\t<dd><dl>\n",
       "\t<dt>$outputNObs</dt>\n",
       "\t\t<dd>5960</dd>\n",
       "\t<dt>$outputNVars</dt>\n",
       "\t\t<dd>26</dd>\n",
       "</dl>\n",
       "</dd>\n",
       "\t<dt>$SRSFreq</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 1 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>NObs</th><th scope=col>NSamp</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>5960</td><td>1788</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$OutputCasTables</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 1 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>casLib</th><th scope=col>Name</th><th scope=col>Label</th><th scope=col>Rows</th><th scope=col>Columns</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>CASUSER(sasdemo05)</td><td>hmeq</td><td></td><td>5960</td><td>26</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$outputSize] \\begin{description}\n",
       "\\item[\\$outputNObs] 5960\n",
       "\\item[\\$outputNVars] 26\n",
       "\\end{description}\n",
       "\n",
       "\\item[\\$SRSFreq] A casDataFrame: 1 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " NObs & NSamp\\\\\n",
       " <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 5960 & 1788\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$OutputCasTables] A casDataFrame: 1 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       " casLib & Name & Label & Rows & Columns\\\\\n",
       " <chr> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t CASUSER(sasdemo05) & hmeq &  & 5960 & 26\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$outputSize\n",
       ":   $outputNObs\n",
       ":   5960\n",
       "$outputNVars\n",
       ":   26\n",
       "\n",
       "\n",
       "\n",
       "$SRSFreq\n",
       ":   \n",
       "A casDataFrame: 1 × 2\n",
       "\n",
       "| NObs &lt;dbl&gt; | NSamp &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 5960 | 1788 |\n",
       "\n",
       "\n",
       "$OutputCasTables\n",
       ":   \n",
       "A casDataFrame: 1 × 5\n",
       "\n",
       "| casLib &lt;chr&gt; | Name &lt;chr&gt; | Label &lt;chr&gt; | Rows &lt;dbl&gt; | Columns &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| CASUSER(sasdemo05) | hmeq | <!----> | 5960 | 26 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$outputSize\n",
       "$outputSize$outputNObs\n",
       "[1] 5960\n",
       "\n",
       "$outputSize$outputNVars\n",
       "[1] 26\n",
       "\n",
       "\n",
       "$SRSFreq\n",
       "  NObs NSamp\n",
       "1 5960  1788\n",
       "\n",
       "$OutputCasTables\n",
       "              casLib Name Label Rows Columns\n",
       "1 CASUSER(sasdemo05) hmeq       5960      26\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Partition the data\n",
    "cas.sampling.srs(conn,\n",
    "    table = 'hmeq',\n",
    "    samppct = 30,\n",
    "    partind = TRUE,\n",
    "    output = list(casOut = list(name = 'hmeq', replace = T), copyVars = 'ALL')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let’s map our variables to build reusable labels for our function calls. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note: I do not want to hard code any of my variable names.\n",
    "indata <- 'hmeq'\n",
    "\n",
    "# Get variable info and types\n",
    "colinfo <- head(cas.table.columnInfo(conn, table = indata)$ColumnInfo, -1)\n",
    "\n",
    "# My target variable is the first column\n",
    "target <- colinfo$Column[1]\n",
    "\n",
    "# For models that can inherently handle missing values (ex: Decision Tree)\n",
    "inputs <- colinfo$Column[-1]\n",
    "nominals <- c(target, subset(colinfo, Type == 'varchar')$Column)\n",
    "\n",
    "# For models that can't handle missing values\n",
    "imp_inputs =  c(\"IMP_CLAGE\", \"IMP_CLNO\", \"IMP_DEBTINC\", \"IMP_DELINQ\",  \n",
    "                   \"IMP_DEROG\", \"IMP_LOAN\", \"IMP_MORTDUE\", \"IMP_NINQ\", \"IMP_VALUE\", \n",
    "                   \"IMP_YOJ\", \"IMP_JOB\", \"IMP_REASON\")\n",
    "imp_nominals = c(\"IMP_JOB\", \"IMP_REASON\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build and Score R Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run R models, the data must be taken from the in-memory CAS table and placed into a R data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to in-memory CAS table\n",
    "hmeq1 <- defCasTable(conn, tablename=\"HMEQ\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create CAS DataFrame from CAS table\n",
    "df1 = to.casDataFrame(hmeq1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create R DataFrame from CAS DataFrame\n",
    "df1 = to.data.frame(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename partition indicator variable \n",
    "names(df1)[length(names(df1))]<-\"part\" \n",
    "# Make dummy variables \n",
    "df1$reason_debtcon <- ifelse(df1$REASON == 'DebtCon', 1, 0)\n",
    "df1$job_Office <- ifelse(df1$JOB == \"Office\", 1, 0)\n",
    "df1$job_Mgr <- ifelse(df1$JOB == 'Mgr', 1,  0)\n",
    "df1$job_ProfExe <- ifelse(df1$JOB == 'ProfExe', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep only imputed numeric data and target\n",
    "df2 = subset(df1, select=c(BAD, IMP_CLAGE, IMP_CLNO, IMP_DEBTINC, IMP_DELINQ, \n",
    "                           IMP_DEROG, IMP_LOAN, IMP_MORTDUE, IMP_NINQ, IMP_VALUE, \n",
    "                           IMP_YOJ, part, reason_debtcon, job_Office, job_Mgr, job_ProfExe))\n",
    "# Split into train and test data frames \n",
    "train = subset(df2, part==0)\n",
    "train = subset(train, select = -c(part))\n",
    "test = subset(df2, part==1)\n",
    "test = subset(test, select = -c(part))\n",
    "# Save actual values for test to use in assessment\n",
    "actual = test$BAD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, now let’s begin modeling. I am looking at three models: a logistic regression, a decision tree, and a gradient boosting model. For each model, I will use a R function to build the model and score the test data set, but I will use SAS’s assessment function to make it easy to assess all models side-by-side. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**R Logisic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build logistic regression\n",
    "rlog <- glm(BAD ~ ., family=\"binomial\", data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Score test data \n",
    "rlog_scored <- predict(rlog, test, type=\"response\")\n",
    "# Create dataframe holding predicted values and results\n",
    "rlog_scored  <- cbind(rlog_scored , actual)\n",
    "rlog_scored  <- as.data.frame(rlog_scored)\n",
    "# Save R dataframe to CAS table\n",
    "rlog_scored <- as.casTable(conn, rlog_scored, casOut='rlog_scored')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assess performance\n",
    "rlog_assessed <- cas.percentile.assess(conn,\n",
    "        table    = list(name = 'rlog_scored'),\n",
    "        inputs = 'rlog_scored',\n",
    "        response = 'actual',\n",
    "        event    = '1')\n",
    "rlog_roc <- rlog_assessed$ROCInfo\n",
    "rlog_roc$Model <- \"R Logistic Regression\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**R Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build decision tree\n",
    "rtree <- rpart(BAD ~ ., method=\"class\", data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Score test data \n",
    "rtree_scored <- predict(rtree, test, type=\"prob\")\n",
    "# Create dataframe holding predicted values and results\n",
    "rtree_scored  <- cbind(rtree_scored , actual)\n",
    "rtree_scored  <- as.data.frame(rtree_scored)\n",
    "names(rtree_scored) <- c(\"p_0\", \"p_1\", \"actual\")\n",
    "rtree_scored <- subset(rtree_scored, select = -c(p_0))\n",
    "# Save R dataframe to CAS table\n",
    "rtree_scored <- as.casTable(conn, rtree_scored, casOut='rtree_scored')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assess performance\n",
    "rtree_assessed <- cas.percentile.assess(conn,\n",
    "        table    = list(name = 'rtree_scored'),\n",
    "        inputs = 'p_1',\n",
    "        response = 'actual',\n",
    "        event    = '1')\n",
    "rtree_roc <- rtree_assessed$ROCInfo\n",
    "rtree_roc$Model <- \"R Decision Tree\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**R Gradient Boosting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare data for xgboost package\n",
    "# Saving train target\n",
    "# Test target aleady stored as actual \n",
    "labels <- train$BAD\n",
    "mtrain <- subset(train, select = -c(BAD))\n",
    "mtest <- subset(test, select = -c(BAD))\n",
    "# Convernting train and test dataframes to matrix\n",
    "mtrain <- as.matrix(mtrain)\n",
    "mtest <- as.matrix(mtest)\n",
    "# Converting train and test matrices to DMtraix \n",
    "xgtrain <- xgb.DMatrix(data=mtrain, label=labels)\n",
    "xgtest <- xgb.DMatrix(data=mtest, label=actual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttrain-error:0.104746 \n",
      "[2]\ttrain-error:0.103068 \n",
      "[3]\ttrain-error:0.096596 \n",
      "[4]\ttrain-error:0.093241 \n",
      "[5]\ttrain-error:0.086769 \n",
      "[6]\ttrain-error:0.081975 \n",
      "[7]\ttrain-error:0.079338 \n",
      "[8]\ttrain-error:0.078140 \n",
      "[9]\ttrain-error:0.073346 \n",
      "[10]\ttrain-error:0.072867 \n",
      "[11]\ttrain-error:0.069271 \n",
      "[12]\ttrain-error:0.066155 \n",
      "[13]\ttrain-error:0.061601 \n",
      "[14]\ttrain-error:0.061361 \n",
      "[15]\ttrain-error:0.059204 \n",
      "[16]\ttrain-error:0.055369 \n",
      "[17]\ttrain-error:0.052493 \n",
      "[18]\ttrain-error:0.050815 \n",
      "[19]\ttrain-error:0.048418 \n",
      "[20]\ttrain-error:0.047220 \n",
      "[21]\ttrain-error:0.043384 \n",
      "[22]\ttrain-error:0.042665 \n",
      "[23]\ttrain-error:0.041227 \n",
      "[24]\ttrain-error:0.039070 \n",
      "[25]\ttrain-error:0.037872 \n",
      "[26]\ttrain-error:0.037152 \n",
      "[27]\ttrain-error:0.034516 \n",
      "[28]\ttrain-error:0.031640 \n",
      "[29]\ttrain-error:0.029003 \n",
      "[30]\ttrain-error:0.028044 \n",
      "[31]\ttrain-error:0.025647 \n",
      "[32]\ttrain-error:0.024928 \n",
      "[33]\ttrain-error:0.025407 \n",
      "[34]\ttrain-error:0.023250 \n",
      "[35]\ttrain-error:0.023250 \n",
      "[36]\ttrain-error:0.021812 \n",
      "[37]\ttrain-error:0.020853 \n",
      "[38]\ttrain-error:0.018696 \n",
      "[39]\ttrain-error:0.018217 \n",
      "[40]\ttrain-error:0.017977 \n",
      "[41]\ttrain-error:0.017498 \n",
      "[42]\ttrain-error:0.016059 \n",
      "[43]\ttrain-error:0.015101 \n",
      "[44]\ttrain-error:0.015580 \n",
      "[45]\ttrain-error:0.015340 \n",
      "[46]\ttrain-error:0.013663 \n",
      "[47]\ttrain-error:0.013183 \n",
      "[48]\ttrain-error:0.012943 \n",
      "[49]\ttrain-error:0.011985 \n",
      "[50]\ttrain-error:0.011266 \n",
      "[51]\ttrain-error:0.011505 \n",
      "[52]\ttrain-error:0.011266 \n",
      "[53]\ttrain-error:0.011026 \n",
      "[54]\ttrain-error:0.010786 \n",
      "[55]\ttrain-error:0.010067 \n",
      "[56]\ttrain-error:0.010067 \n",
      "[57]\ttrain-error:0.009827 \n",
      "[58]\ttrain-error:0.008389 \n",
      "[59]\ttrain-error:0.008869 \n",
      "[60]\ttrain-error:0.008869 \n",
      "[61]\ttrain-error:0.008629 \n",
      "[62]\ttrain-error:0.006232 \n",
      "[63]\ttrain-error:0.005992 \n",
      "[64]\ttrain-error:0.006232 \n",
      "[65]\ttrain-error:0.006232 \n",
      "[66]\ttrain-error:0.006232 \n",
      "[67]\ttrain-error:0.005992 \n",
      "[68]\ttrain-error:0.005273 \n",
      "[69]\ttrain-error:0.004794 \n",
      "[70]\ttrain-error:0.004314 \n",
      "[71]\ttrain-error:0.003595 \n",
      "[72]\ttrain-error:0.003835 \n",
      "[73]\ttrain-error:0.003356 \n",
      "[74]\ttrain-error:0.002876 \n",
      "[75]\ttrain-error:0.001918 \n",
      "[76]\ttrain-error:0.001918 \n",
      "[77]\ttrain-error:0.001918 \n",
      "[78]\ttrain-error:0.001918 \n",
      "[79]\ttrain-error:0.001918 \n",
      "[80]\ttrain-error:0.001918 \n",
      "[81]\ttrain-error:0.001918 \n",
      "[82]\ttrain-error:0.001918 \n",
      "[83]\ttrain-error:0.001678 \n",
      "[84]\ttrain-error:0.001438 \n",
      "[85]\ttrain-error:0.001438 \n",
      "[86]\ttrain-error:0.001198 \n",
      "[87]\ttrain-error:0.000959 \n",
      "[88]\ttrain-error:0.000959 \n",
      "[89]\ttrain-error:0.000959 \n",
      "[90]\ttrain-error:0.000959 \n",
      "[91]\ttrain-error:0.000959 \n",
      "[92]\ttrain-error:0.000719 \n",
      "[93]\ttrain-error:0.000719 \n",
      "[94]\ttrain-error:0.000959 \n",
      "[95]\ttrain-error:0.000959 \n",
      "[96]\ttrain-error:0.000719 \n",
      "[97]\ttrain-error:0.000719 \n",
      "[98]\ttrain-error:0.000479 \n",
      "[99]\ttrain-error:0.000479 \n",
      "[100]\ttrain-error:0.000479 \n"
     ]
    }
   ],
   "source": [
    "# Build gradient boosting\n",
    "rboost <- xgboost(data = xgtrain, label = labels, objective = \"binary:logistic\", nrounds = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Score test data\n",
    "rboost_scored <- predict(rboost, xgtest)\n",
    "# Create dataframe holding predicted values and results\n",
    "rboost_scored <- data.frame(rboost_scored, actual)\n",
    "rboost_scored <- as.data.frame(rboost_scored)\n",
    "# Save R dataframe to CAS table\n",
    "rboost_scored <- as.casTable(conn, rboost_scored, casOut='rboost_scored')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assess performance\n",
    "rboost_assessed <- cas.percentile.assess(conn,\n",
    "        table    = list(name = 'rboost_scored'),\n",
    "        inputs = 'rboost_scored',\n",
    "        response = 'actual',\n",
    "        event    = '1')\n",
    "rboost_roc <- rboost_assessed$ROCInfo\n",
    "rboost_roc$Model <- \"R Gradient Boosting\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build and Score CAS Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can do the same thing in CAS: build, score, and assess a logistic regression, a decision tree, and a gradient boosting model. In addition, CAS has the option to autotune the tree-based models, so I would like to include an example of that as well! Autotuning finds the best combination of hyperparameter to increase model accuracy.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CAS Logisic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$ModelInfo</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 5 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>RowId</th><th scope=col>Description</th><th scope=col>Value</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>DATA       </td><td>Data Source           </td><td>HMEQ                       </td></tr>\n",
       "\t<tr><td>RESPONSEVAR</td><td>Response Variable     </td><td>BAD                        </td></tr>\n",
       "\t<tr><td>DIST       </td><td>Distribution          </td><td>Binary                     </td></tr>\n",
       "\t<tr><td>LINK       </td><td>Link Function         </td><td>Logit                      </td></tr>\n",
       "\t<tr><td>TECH       </td><td>Optimization Technique</td><td>Newton-Raphson with Ridging</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$NObs</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 2 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>RowId</th><th scope=col>Description</th><th scope=col>Value</th><th scope=col>Training</th><th scope=col>Validation</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>NREAD</td><td>Number of Observations Read</td><td>5960</td><td>4172</td><td>1788</td></tr>\n",
       "\t<tr><td>NUSED</td><td>Number of Observations Used</td><td>5960</td><td>4172</td><td>1788</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$ResponseProfile</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 2 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>OrderedValue</th><th scope=col>Outcome</th><th scope=col>BAD</th><th scope=col>Freq</th><th scope=col>Training</th><th scope=col>Validation</th><th scope=col>Modeled</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>0</td><td>0</td><td>4771</td><td>3331</td><td>1440</td><td>*</td></tr>\n",
       "\t<tr><td>2</td><td>1</td><td>1</td><td>1189</td><td> 841</td><td> 348</td><td> </td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$ClassInfo</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 2 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Class</th><th scope=col>Levels</th><th scope=col>Values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>IMP_JOB   </td><td>6</td><td>Mgr Office Other ProfExe Sales Self</td></tr>\n",
       "\t<tr><td>IMP_REASON</td><td>2</td><td>DebtCon HomeImp                    </td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SelectionInfo</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 5 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>RowId</th><th scope=col>Description</th><th scope=col>Value</th><th scope=col>NValue</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>METHOD       </td><td>Selection Method         </td><td>Backward</td><td>NaN</td></tr>\n",
       "\t<tr><td>SELCRITERION </td><td>Select Criterion         </td><td>SBC     </td><td>NaN</td></tr>\n",
       "\t<tr><td>STOPCRITERION</td><td>Stop Criterion           </td><td>SBC     </td><td>NaN</td></tr>\n",
       "\t<tr><td>HIERARCHY    </td><td>Effect Hierarchy Enforced</td><td>Single  </td><td>NaN</td></tr>\n",
       "\t<tr><td>STOPHORIZON  </td><td>Stop Horizon             </td><td>3       </td><td>  3</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$Summary.ConvergenceStatus</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 1 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Reason</th><th scope=col>Status</th><th scope=col>MaxGradient</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Convergence criterion (FCONV=1E-7) satisfied.</td><td>0</td><td>0.0009467803</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$Summary.SelectionSummary</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 7 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Control</th><th scope=col>Step</th><th scope=col>EffectRemoved</th><th scope=col>nEffectsIn</th><th scope=col>SBC</th><th scope=col>OptSBC</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td> </td><td>0</td><td>           </td><td>13</td><td>3363.055</td><td>0</td></tr>\n",
       "\t<tr><td>-</td><td>1</td><td>IMP_YOJ    </td><td>12</td><td>3357.863</td><td>0</td></tr>\n",
       "\t<tr><td> </td><td>2</td><td>IMP_JOB    </td><td>11</td><td>3352.974</td><td>0</td></tr>\n",
       "\t<tr><td> </td><td>3</td><td>IMP_MORTDUE</td><td>10</td><td>3349.044</td><td>1</td></tr>\n",
       "\t<tr><td> </td><td>4</td><td>IMP_REASON </td><td> 9</td><td>3349.604</td><td>0</td></tr>\n",
       "\t<tr><td> </td><td>5</td><td>IMP_CLNO   </td><td> 8</td><td>3354.464</td><td>0</td></tr>\n",
       "\t<tr><td> </td><td>6</td><td>IMP_LOAN   </td><td> 7</td><td>3372.209</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$Summary.StopReason</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 1 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Reason</th><th scope=col>Code</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Selection stopped at a local minimum of the STOP criterion.</td><td>6</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$Summary.SelectionReason</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 1 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Reason</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>The model at step 3 is selected.</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$Summary.SelectedEffects</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 1 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Label</th><th scope=col>Effects</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Selected Effects:</td><td>Intercept IMP_CLAGE IMP_CLNO IMP_DEBTINC IMP_DELINQ IMP_DEROG IMP_LOAN IMP_NINQ IMP_VALUE IMP_REASON</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SelectedModel.Dimensions</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 5 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>RowId</th><th scope=col>Description</th><th scope=col>Value</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>NDESIGNCOLS</td><td>Columns in Design         </td><td>11</td></tr>\n",
       "\t<tr><td>NEFFECTS   </td><td>Number of Effects         </td><td>10</td></tr>\n",
       "\t<tr><td>MAXEFCOLS  </td><td>Max Effect Columns        </td><td> 2</td></tr>\n",
       "\t<tr><td>DESIGNRANK </td><td>Rank of Design            </td><td>10</td></tr>\n",
       "\t<tr><td>OPTPARM    </td><td>Parameters in Optimization</td><td>10</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SelectedModel.GlobalTest</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 1 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Test</th><th scope=col>DF</th><th scope=col>ChiSq</th><th scope=col>ProbChiSq</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Likelihood Ratio</td><td>9</td><td>927.8438</td><td>6.187011e-194</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SelectedModel.FitStatistics</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 11 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>RowId</th><th scope=col>Description</th><th scope=col>Training</th><th scope=col>Validation</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>M2LL    </td><td>-2 Log Likelihood        </td><td>3265.7480145</td><td>1404.7441937</td></tr>\n",
       "\t<tr><td>AIC     </td><td>AIC (smaller is better)  </td><td>3285.7480145</td><td>1424.7441937</td></tr>\n",
       "\t<tr><td>AICC    </td><td>AICC (smaller is better) </td><td>3285.8008864</td><td>1424.8679978</td></tr>\n",
       "\t<tr><td>SBC     </td><td>SBC (smaller is better)  </td><td>3349.1095227</td><td>1479.6327232</td></tr>\n",
       "\t<tr><td>ASE     </td><td>Average Square Error     </td><td>   0.1208238</td><td>   0.1208004</td></tr>\n",
       "\t<tr><td>M2LLNULL</td><td>-2 Log L (Intercept-only)</td><td>4193.5917796</td><td>1762.4978732</td></tr>\n",
       "\t<tr><td>RSQUARE </td><td>R-Square                 </td><td>   0.1994032</td><td>   0.1813396</td></tr>\n",
       "\t<tr><td>ADJRSQ  </td><td>Max-rescaled R-Square    </td><td>   0.3145064</td><td>   0.2892936</td></tr>\n",
       "\t<tr><td>MCFADDEN</td><td>McFadden's R-Square      </td><td>   0.2212528</td><td>   0.2029811</td></tr>\n",
       "\t<tr><td>MISCLASS</td><td>Misclassification Rate   </td><td>   0.1598754</td><td>   0.1610738</td></tr>\n",
       "\t<tr><td>DIFFMEAN</td><td>Difference of Means      </td><td>   0.2505009</td><td>   0.2394360</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SelectedModel.ParameterEstimates</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 11 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Effect</th><th scope=col>IMP_REASON</th><th scope=col>Parameter</th><th scope=col>ParmName</th><th scope=col>DF</th><th scope=col>Estimate</th><th scope=col>StdErr</th><th scope=col>ChiSq</th><th scope=col>ProbChiSq</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Intercept  </td><td>       </td><td>Intercept         </td><td>Intercept         </td><td>1</td><td> 2.621205e+00</td><td>2.886291e-01</td><td> 82.474890</td><td>1.070211e-19</td></tr>\n",
       "\t<tr><td>IMP_CLAGE  </td><td>       </td><td>IMP_CLAGE         </td><td>IMP_CLAGE         </td><td>1</td><td> 5.607892e-03</td><td>6.503098e-04</td><td> 74.363313</td><td>6.498554e-18</td></tr>\n",
       "\t<tr><td>IMP_CLNO   </td><td>       </td><td>IMP_CLNO          </td><td>IMP_CLNO          </td><td>1</td><td> 1.606460e-02</td><td>4.998012e-03</td><td> 10.331073</td><td>1.308091e-03</td></tr>\n",
       "\t<tr><td>IMP_DEBTINC</td><td>       </td><td>IMP_DEBTINC       </td><td>IMP_DEBTINC       </td><td>1</td><td>-6.249575e-02</td><td>7.415592e-03</td><td> 71.024674</td><td>3.527853e-17</td></tr>\n",
       "\t<tr><td>IMP_DELINQ </td><td>       </td><td>IMP_DELINQ        </td><td>IMP_DELINQ        </td><td>1</td><td>-7.563151e-01</td><td>4.540623e-02</td><td>277.443533</td><td>2.708342e-62</td></tr>\n",
       "\t<tr><td>IMP_DEROG  </td><td>       </td><td>IMP_DEROG         </td><td>IMP_DEROG         </td><td>1</td><td>-6.573445e-01</td><td>5.903230e-02</td><td>123.995732</td><td>8.442498e-29</td></tr>\n",
       "\t<tr><td>IMP_LOAN   </td><td>       </td><td>IMP_LOAN          </td><td>IMP_LOAN          </td><td>1</td><td> 2.135606e-05</td><td>4.873157e-06</td><td> 19.205309</td><td>1.173865e-05</td></tr>\n",
       "\t<tr><td>IMP_NINQ   </td><td>       </td><td>IMP_NINQ          </td><td>IMP_NINQ          </td><td>1</td><td>-1.649363e-01</td><td>2.431238e-02</td><td> 46.023296</td><td>1.168551e-11</td></tr>\n",
       "\t<tr><td>IMP_VALUE  </td><td>       </td><td>IMP_VALUE         </td><td>IMP_VALUE         </td><td>1</td><td>-1.258613e-06</td><td>9.087475e-07</td><td>  1.918219</td><td>1.660531e-01</td></tr>\n",
       "\t<tr><td>IMP_REASON </td><td>DebtCon</td><td>IMP_REASON DebtCon</td><td>IMP_REASON_DebtCon</td><td>1</td><td> 2.940699e-01</td><td>9.895972e-02</td><td>  8.830477</td><td>2.962409e-03</td></tr>\n",
       "\t<tr><td>IMP_REASON </td><td>HomeImp</td><td>IMP_REASON HomeImp</td><td>IMP_REASON_HomeImp</td><td>0</td><td> 0.000000e+00</td><td>         NaN</td><td>       NaN</td><td>         NaN</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$Timing</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 9 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>RowId</th><th scope=col>Task</th><th scope=col>Time</th><th scope=col>RelTime</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>SETUP         </td><td>Setup and Parsing   </td><td>0.0409197807</td><td>0.068626965</td></tr>\n",
       "\t<tr><td>LEVELIZATION  </td><td>Levelization        </td><td>0.0499210358</td><td>0.083723058</td></tr>\n",
       "\t<tr><td>INITIALIZATION</td><td>Model Initialization</td><td>0.0207328796</td><td>0.034771315</td></tr>\n",
       "\t<tr><td>SSCP          </td><td>SSCP Computation    </td><td>0.0463039875</td><td>0.077656871</td></tr>\n",
       "\t<tr><td>FITTING       </td><td>Model Selection     </td><td>0.3005890846</td><td>0.504120897</td></tr>\n",
       "\t<tr><td>OUTPUT        </td><td>Creating Output Data</td><td>0.0226120949</td><td>0.037922966</td></tr>\n",
       "\t<tr><td>STORE         </td><td>Model Storing       </td><td>0.1156239510</td><td>0.193914060</td></tr>\n",
       "\t<tr><td>CLEANUP       </td><td>Cleanup             </td><td>0.0008149147</td><td>0.001366701</td></tr>\n",
       "\t<tr><td>TOTAL         </td><td>Total               </td><td>0.5962638855</td><td>1.000000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$OutputCasTables</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 2 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>casLib</th><th scope=col>Name</th><th scope=col>Label</th><th scope=col>Rows</th><th scope=col>Columns</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>CASUSER(sasdemo05)</td><td>log_model</td><td></td><td>   1</td><td>2</td></tr>\n",
       "\t<tr><td>CASUSER(sasdemo05)</td><td>log_score</td><td></td><td>5960</td><td>5</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$ModelInfo] A casDataFrame: 5 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       " RowId & Description & Value\\\\\n",
       " <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t DATA        & Data Source            & HMEQ                       \\\\\n",
       "\t RESPONSEVAR & Response Variable      & BAD                        \\\\\n",
       "\t DIST        & Distribution           & Binary                     \\\\\n",
       "\t LINK        & Link Function          & Logit                      \\\\\n",
       "\t TECH        & Optimization Technique & Newton-Raphson with Ridging\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$NObs] A casDataFrame: 2 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       " RowId & Description & Value & Training & Validation\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t NREAD & Number of Observations Read & 5960 & 4172 & 1788\\\\\n",
       "\t NUSED & Number of Observations Used & 5960 & 4172 & 1788\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$ResponseProfile] A casDataFrame: 2 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       " OrderedValue & Outcome & BAD & Freq & Training & Validation & Modeled\\\\\n",
       " <int> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1 & 0 & 0 & 4771 & 3331 & 1440 & *\\\\\n",
       "\t 2 & 1 & 1 & 1189 &  841 &  348 &  \\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$ClassInfo] A casDataFrame: 2 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       " Class & Levels & Values\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t IMP\\_JOB    & 6 & Mgr Office Other ProfExe Sales Self\\\\\n",
       "\t IMP\\_REASON & 2 & DebtCon HomeImp                    \\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SelectionInfo] A casDataFrame: 5 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " RowId & Description & Value & NValue\\\\\n",
       " <chr> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t METHOD        & Selection Method          & Backward & NaN\\\\\n",
       "\t SELCRITERION  & Select Criterion          & SBC      & NaN\\\\\n",
       "\t STOPCRITERION & Stop Criterion            & SBC      & NaN\\\\\n",
       "\t HIERARCHY     & Effect Hierarchy Enforced & Single   & NaN\\\\\n",
       "\t STOPHORIZON   & Stop Horizon              & 3        &   3\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$Summary.ConvergenceStatus] A casDataFrame: 1 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       " Reason & Status & MaxGradient\\\\\n",
       " <chr> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Convergence criterion (FCONV=1E-7) satisfied. & 0 & 0.0009467803\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$Summary.SelectionSummary] A casDataFrame: 7 × 6\n",
       "\\begin{tabular}{r|llllll}\n",
       " Control & Step & EffectRemoved & nEffectsIn & SBC & OptSBC\\\\\n",
       " <chr> & <int> & <chr> & <int> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t   & 0 &             & 13 & 3363.055 & 0\\\\\n",
       "\t - & 1 & IMP\\_YOJ     & 12 & 3357.863 & 0\\\\\n",
       "\t   & 2 & IMP\\_JOB     & 11 & 3352.974 & 0\\\\\n",
       "\t   & 3 & IMP\\_MORTDUE & 10 & 3349.044 & 1\\\\\n",
       "\t   & 4 & IMP\\_REASON  &  9 & 3349.604 & 0\\\\\n",
       "\t   & 5 & IMP\\_CLNO    &  8 & 3354.464 & 0\\\\\n",
       "\t   & 6 & IMP\\_LOAN    &  7 & 3372.209 & 0\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$Summary.StopReason] A casDataFrame: 1 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " Reason & Code\\\\\n",
       " <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t Selection stopped at a local minimum of the STOP criterion. & 6\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$Summary.SelectionReason] A casDataFrame: 1 × 1\n",
       "\\begin{tabular}{r|l}\n",
       " Reason\\\\\n",
       " <chr>\\\\\n",
       "\\hline\n",
       "\t The model at step 3 is selected.\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$Summary.SelectedEffects] A casDataFrame: 1 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " Label & Effects\\\\\n",
       " <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Selected Effects: & Intercept IMP\\_CLAGE IMP\\_CLNO IMP\\_DEBTINC IMP\\_DELINQ IMP\\_DEROG IMP\\_LOAN IMP\\_NINQ IMP\\_VALUE IMP\\_REASON\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SelectedModel.Dimensions] A casDataFrame: 5 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       " RowId & Description & Value\\\\\n",
       " <chr> & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t NDESIGNCOLS & Columns in Design          & 11\\\\\n",
       "\t NEFFECTS    & Number of Effects          & 10\\\\\n",
       "\t MAXEFCOLS   & Max Effect Columns         &  2\\\\\n",
       "\t DESIGNRANK  & Rank of Design             & 10\\\\\n",
       "\t OPTPARM     & Parameters in Optimization & 10\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SelectedModel.GlobalTest] A casDataFrame: 1 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " Test & DF & ChiSq & ProbChiSq\\\\\n",
       " <chr> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Likelihood Ratio & 9 & 927.8438 & 6.187011e-194\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SelectedModel.FitStatistics] A casDataFrame: 11 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " RowId & Description & Training & Validation\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t M2LL     & -2 Log Likelihood         & 3265.7480145 & 1404.7441937\\\\\n",
       "\t AIC      & AIC (smaller is better)   & 3285.7480145 & 1424.7441937\\\\\n",
       "\t AICC     & AICC (smaller is better)  & 3285.8008864 & 1424.8679978\\\\\n",
       "\t SBC      & SBC (smaller is better)   & 3349.1095227 & 1479.6327232\\\\\n",
       "\t ASE      & Average Square Error      &    0.1208238 &    0.1208004\\\\\n",
       "\t M2LLNULL & -2 Log L (Intercept-only) & 4193.5917796 & 1762.4978732\\\\\n",
       "\t RSQUARE  & R-Square                  &    0.1994032 &    0.1813396\\\\\n",
       "\t ADJRSQ   & Max-rescaled R-Square     &    0.3145064 &    0.2892936\\\\\n",
       "\t MCFADDEN & McFadden's R-Square       &    0.2212528 &    0.2029811\\\\\n",
       "\t MISCLASS & Misclassification Rate    &    0.1598754 &    0.1610738\\\\\n",
       "\t DIFFMEAN & Difference of Means       &    0.2505009 &    0.2394360\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SelectedModel.ParameterEstimates] A casDataFrame: 11 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       " Effect & IMP\\_REASON & Parameter & ParmName & DF & Estimate & StdErr & ChiSq & ProbChiSq\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <int> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Intercept   &         & Intercept          & Intercept          & 1 &  2.621205e+00 & 2.886291e-01 &  82.474890 & 1.070211e-19\\\\\n",
       "\t IMP\\_CLAGE   &         & IMP\\_CLAGE          & IMP\\_CLAGE          & 1 &  5.607892e-03 & 6.503098e-04 &  74.363313 & 6.498554e-18\\\\\n",
       "\t IMP\\_CLNO    &         & IMP\\_CLNO           & IMP\\_CLNO           & 1 &  1.606460e-02 & 4.998012e-03 &  10.331073 & 1.308091e-03\\\\\n",
       "\t IMP\\_DEBTINC &         & IMP\\_DEBTINC        & IMP\\_DEBTINC        & 1 & -6.249575e-02 & 7.415592e-03 &  71.024674 & 3.527853e-17\\\\\n",
       "\t IMP\\_DELINQ  &         & IMP\\_DELINQ         & IMP\\_DELINQ         & 1 & -7.563151e-01 & 4.540623e-02 & 277.443533 & 2.708342e-62\\\\\n",
       "\t IMP\\_DEROG   &         & IMP\\_DEROG          & IMP\\_DEROG          & 1 & -6.573445e-01 & 5.903230e-02 & 123.995732 & 8.442498e-29\\\\\n",
       "\t IMP\\_LOAN    &         & IMP\\_LOAN           & IMP\\_LOAN           & 1 &  2.135606e-05 & 4.873157e-06 &  19.205309 & 1.173865e-05\\\\\n",
       "\t IMP\\_NINQ    &         & IMP\\_NINQ           & IMP\\_NINQ           & 1 & -1.649363e-01 & 2.431238e-02 &  46.023296 & 1.168551e-11\\\\\n",
       "\t IMP\\_VALUE   &         & IMP\\_VALUE          & IMP\\_VALUE          & 1 & -1.258613e-06 & 9.087475e-07 &   1.918219 & 1.660531e-01\\\\\n",
       "\t IMP\\_REASON  & DebtCon & IMP\\_REASON DebtCon & IMP\\_REASON\\_DebtCon & 1 &  2.940699e-01 & 9.895972e-02 &   8.830477 & 2.962409e-03\\\\\n",
       "\t IMP\\_REASON  & HomeImp & IMP\\_REASON HomeImp & IMP\\_REASON\\_HomeImp & 0 &  0.000000e+00 &          NaN &        NaN &          NaN\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$Timing] A casDataFrame: 9 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " RowId & Task & Time & RelTime\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t SETUP          & Setup and Parsing    & 0.0409197807 & 0.068626965\\\\\n",
       "\t LEVELIZATION   & Levelization         & 0.0499210358 & 0.083723058\\\\\n",
       "\t INITIALIZATION & Model Initialization & 0.0207328796 & 0.034771315\\\\\n",
       "\t SSCP           & SSCP Computation     & 0.0463039875 & 0.077656871\\\\\n",
       "\t FITTING        & Model Selection      & 0.3005890846 & 0.504120897\\\\\n",
       "\t OUTPUT         & Creating Output Data & 0.0226120949 & 0.037922966\\\\\n",
       "\t STORE          & Model Storing        & 0.1156239510 & 0.193914060\\\\\n",
       "\t CLEANUP        & Cleanup              & 0.0008149147 & 0.001366701\\\\\n",
       "\t TOTAL          & Total                & 0.5962638855 & 1.000000000\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$OutputCasTables] A casDataFrame: 2 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       " casLib & Name & Label & Rows & Columns\\\\\n",
       " <chr> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t CASUSER(sasdemo05) & log\\_model &  &    1 & 2\\\\\n",
       "\t CASUSER(sasdemo05) & log\\_score &  & 5960 & 5\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$ModelInfo\n",
       ":   \n",
       "A casDataFrame: 5 × 3\n",
       "\n",
       "| RowId &lt;chr&gt; | Description &lt;chr&gt; | Value &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| DATA        | Data Source            | HMEQ                        |\n",
       "| RESPONSEVAR | Response Variable      | BAD                         |\n",
       "| DIST        | Distribution           | Binary                      |\n",
       "| LINK        | Link Function          | Logit                       |\n",
       "| TECH        | Optimization Technique | Newton-Raphson with Ridging |\n",
       "\n",
       "\n",
       "$NObs\n",
       ":   \n",
       "A casDataFrame: 2 × 5\n",
       "\n",
       "| RowId &lt;chr&gt; | Description &lt;chr&gt; | Value &lt;dbl&gt; | Training &lt;dbl&gt; | Validation &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| NREAD | Number of Observations Read | 5960 | 4172 | 1788 |\n",
       "| NUSED | Number of Observations Used | 5960 | 4172 | 1788 |\n",
       "\n",
       "\n",
       "$ResponseProfile\n",
       ":   \n",
       "A casDataFrame: 2 × 7\n",
       "\n",
       "| OrderedValue &lt;int&gt; | Outcome &lt;chr&gt; | BAD &lt;dbl&gt; | Freq &lt;dbl&gt; | Training &lt;dbl&gt; | Validation &lt;dbl&gt; | Modeled &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 1 | 0 | 0 | 4771 | 3331 | 1440 | * |\n",
       "| 2 | 1 | 1 | 1189 |  841 |  348 | <!----> |\n",
       "\n",
       "\n",
       "$ClassInfo\n",
       ":   \n",
       "A casDataFrame: 2 × 3\n",
       "\n",
       "| Class &lt;chr&gt; | Levels &lt;dbl&gt; | Values &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| IMP_JOB    | 6 | Mgr Office Other ProfExe Sales Self |\n",
       "| IMP_REASON | 2 | DebtCon HomeImp                     |\n",
       "\n",
       "\n",
       "$SelectionInfo\n",
       ":   \n",
       "A casDataFrame: 5 × 4\n",
       "\n",
       "| RowId &lt;chr&gt; | Description &lt;chr&gt; | Value &lt;chr&gt; | NValue &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| METHOD        | Selection Method          | Backward | NaN |\n",
       "| SELCRITERION  | Select Criterion          | SBC      | NaN |\n",
       "| STOPCRITERION | Stop Criterion            | SBC      | NaN |\n",
       "| HIERARCHY     | Effect Hierarchy Enforced | Single   | NaN |\n",
       "| STOPHORIZON   | Stop Horizon              | 3        |   3 |\n",
       "\n",
       "\n",
       "$Summary.ConvergenceStatus\n",
       ":   \n",
       "A casDataFrame: 1 × 3\n",
       "\n",
       "| Reason &lt;chr&gt; | Status &lt;int&gt; | MaxGradient &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| Convergence criterion (FCONV=1E-7) satisfied. | 0 | 0.0009467803 |\n",
       "\n",
       "\n",
       "$Summary.SelectionSummary\n",
       ":   \n",
       "A casDataFrame: 7 × 6\n",
       "\n",
       "| Control &lt;chr&gt; | Step &lt;int&gt; | EffectRemoved &lt;chr&gt; | nEffectsIn &lt;int&gt; | SBC &lt;dbl&gt; | OptSBC &lt;int&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| <!----> | 0 | <!----> | 13 | 3363.055 | 0 |\n",
       "| - | 1 | IMP_YOJ     | 12 | 3357.863 | 0 |\n",
       "| <!----> | 2 | IMP_JOB     | 11 | 3352.974 | 0 |\n",
       "| <!----> | 3 | IMP_MORTDUE | 10 | 3349.044 | 1 |\n",
       "| <!----> | 4 | IMP_REASON  |  9 | 3349.604 | 0 |\n",
       "| <!----> | 5 | IMP_CLNO    |  8 | 3354.464 | 0 |\n",
       "| <!----> | 6 | IMP_LOAN    |  7 | 3372.209 | 0 |\n",
       "\n",
       "\n",
       "$Summary.StopReason\n",
       ":   \n",
       "A casDataFrame: 1 × 2\n",
       "\n",
       "| Reason &lt;chr&gt; | Code &lt;int&gt; |\n",
       "|---|---|\n",
       "| Selection stopped at a local minimum of the STOP criterion. | 6 |\n",
       "\n",
       "\n",
       "$Summary.SelectionReason\n",
       ":   \n",
       "A casDataFrame: 1 × 1\n",
       "\n",
       "| Reason &lt;chr&gt; |\n",
       "|---|\n",
       "| The model at step 3 is selected. |\n",
       "\n",
       "\n",
       "$Summary.SelectedEffects\n",
       ":   \n",
       "A casDataFrame: 1 × 2\n",
       "\n",
       "| Label &lt;chr&gt; | Effects &lt;chr&gt; |\n",
       "|---|---|\n",
       "| Selected Effects: | Intercept IMP_CLAGE IMP_CLNO IMP_DEBTINC IMP_DELINQ IMP_DEROG IMP_LOAN IMP_NINQ IMP_VALUE IMP_REASON |\n",
       "\n",
       "\n",
       "$SelectedModel.Dimensions\n",
       ":   \n",
       "A casDataFrame: 5 × 3\n",
       "\n",
       "| RowId &lt;chr&gt; | Description &lt;chr&gt; | Value &lt;int&gt; |\n",
       "|---|---|---|\n",
       "| NDESIGNCOLS | Columns in Design          | 11 |\n",
       "| NEFFECTS    | Number of Effects          | 10 |\n",
       "| MAXEFCOLS   | Max Effect Columns         |  2 |\n",
       "| DESIGNRANK  | Rank of Design             | 10 |\n",
       "| OPTPARM     | Parameters in Optimization | 10 |\n",
       "\n",
       "\n",
       "$SelectedModel.GlobalTest\n",
       ":   \n",
       "A casDataFrame: 1 × 4\n",
       "\n",
       "| Test &lt;chr&gt; | DF &lt;int&gt; | ChiSq &lt;dbl&gt; | ProbChiSq &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| Likelihood Ratio | 9 | 927.8438 | 6.187011e-194 |\n",
       "\n",
       "\n",
       "$SelectedModel.FitStatistics\n",
       ":   \n",
       "A casDataFrame: 11 × 4\n",
       "\n",
       "| RowId &lt;chr&gt; | Description &lt;chr&gt; | Training &lt;dbl&gt; | Validation &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| M2LL     | -2 Log Likelihood         | 3265.7480145 | 1404.7441937 |\n",
       "| AIC      | AIC (smaller is better)   | 3285.7480145 | 1424.7441937 |\n",
       "| AICC     | AICC (smaller is better)  | 3285.8008864 | 1424.8679978 |\n",
       "| SBC      | SBC (smaller is better)   | 3349.1095227 | 1479.6327232 |\n",
       "| ASE      | Average Square Error      |    0.1208238 |    0.1208004 |\n",
       "| M2LLNULL | -2 Log L (Intercept-only) | 4193.5917796 | 1762.4978732 |\n",
       "| RSQUARE  | R-Square                  |    0.1994032 |    0.1813396 |\n",
       "| ADJRSQ   | Max-rescaled R-Square     |    0.3145064 |    0.2892936 |\n",
       "| MCFADDEN | McFadden's R-Square       |    0.2212528 |    0.2029811 |\n",
       "| MISCLASS | Misclassification Rate    |    0.1598754 |    0.1610738 |\n",
       "| DIFFMEAN | Difference of Means       |    0.2505009 |    0.2394360 |\n",
       "\n",
       "\n",
       "$SelectedModel.ParameterEstimates\n",
       ":   \n",
       "A casDataFrame: 11 × 9\n",
       "\n",
       "| Effect &lt;chr&gt; | IMP_REASON &lt;chr&gt; | Parameter &lt;chr&gt; | ParmName &lt;chr&gt; | DF &lt;int&gt; | Estimate &lt;dbl&gt; | StdErr &lt;dbl&gt; | ChiSq &lt;dbl&gt; | ProbChiSq &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| Intercept   | <!----> | Intercept          | Intercept          | 1 |  2.621205e+00 | 2.886291e-01 |  82.474890 | 1.070211e-19 |\n",
       "| IMP_CLAGE   | <!----> | IMP_CLAGE          | IMP_CLAGE          | 1 |  5.607892e-03 | 6.503098e-04 |  74.363313 | 6.498554e-18 |\n",
       "| IMP_CLNO    | <!----> | IMP_CLNO           | IMP_CLNO           | 1 |  1.606460e-02 | 4.998012e-03 |  10.331073 | 1.308091e-03 |\n",
       "| IMP_DEBTINC | <!----> | IMP_DEBTINC        | IMP_DEBTINC        | 1 | -6.249575e-02 | 7.415592e-03 |  71.024674 | 3.527853e-17 |\n",
       "| IMP_DELINQ  | <!----> | IMP_DELINQ         | IMP_DELINQ         | 1 | -7.563151e-01 | 4.540623e-02 | 277.443533 | 2.708342e-62 |\n",
       "| IMP_DEROG   | <!----> | IMP_DEROG          | IMP_DEROG          | 1 | -6.573445e-01 | 5.903230e-02 | 123.995732 | 8.442498e-29 |\n",
       "| IMP_LOAN    | <!----> | IMP_LOAN           | IMP_LOAN           | 1 |  2.135606e-05 | 4.873157e-06 |  19.205309 | 1.173865e-05 |\n",
       "| IMP_NINQ    | <!----> | IMP_NINQ           | IMP_NINQ           | 1 | -1.649363e-01 | 2.431238e-02 |  46.023296 | 1.168551e-11 |\n",
       "| IMP_VALUE   | <!----> | IMP_VALUE          | IMP_VALUE          | 1 | -1.258613e-06 | 9.087475e-07 |   1.918219 | 1.660531e-01 |\n",
       "| IMP_REASON  | DebtCon | IMP_REASON DebtCon | IMP_REASON_DebtCon | 1 |  2.940699e-01 | 9.895972e-02 |   8.830477 | 2.962409e-03 |\n",
       "| IMP_REASON  | HomeImp | IMP_REASON HomeImp | IMP_REASON_HomeImp | 0 |  0.000000e+00 |          NaN |        NaN |          NaN |\n",
       "\n",
       "\n",
       "$Timing\n",
       ":   \n",
       "A casDataFrame: 9 × 4\n",
       "\n",
       "| RowId &lt;chr&gt; | Task &lt;chr&gt; | Time &lt;dbl&gt; | RelTime &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| SETUP          | Setup and Parsing    | 0.0409197807 | 0.068626965 |\n",
       "| LEVELIZATION   | Levelization         | 0.0499210358 | 0.083723058 |\n",
       "| INITIALIZATION | Model Initialization | 0.0207328796 | 0.034771315 |\n",
       "| SSCP           | SSCP Computation     | 0.0463039875 | 0.077656871 |\n",
       "| FITTING        | Model Selection      | 0.3005890846 | 0.504120897 |\n",
       "| OUTPUT         | Creating Output Data | 0.0226120949 | 0.037922966 |\n",
       "| STORE          | Model Storing        | 0.1156239510 | 0.193914060 |\n",
       "| CLEANUP        | Cleanup              | 0.0008149147 | 0.001366701 |\n",
       "| TOTAL          | Total                | 0.5962638855 | 1.000000000 |\n",
       "\n",
       "\n",
       "$OutputCasTables\n",
       ":   \n",
       "A casDataFrame: 2 × 5\n",
       "\n",
       "| casLib &lt;chr&gt; | Name &lt;chr&gt; | Label &lt;chr&gt; | Rows &lt;dbl&gt; | Columns &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| CASUSER(sasdemo05) | log_model | <!----> |    1 | 2 |\n",
       "| CASUSER(sasdemo05) | log_score | <!----> | 5960 | 5 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$ModelInfo\n",
       "        RowId            Description                       Value\n",
       "1        DATA            Data Source                        HMEQ\n",
       "2 RESPONSEVAR      Response Variable                         BAD\n",
       "3        DIST           Distribution                      Binary\n",
       "4        LINK          Link Function                       Logit\n",
       "5        TECH Optimization Technique Newton-Raphson with Ridging\n",
       "\n",
       "$NObs\n",
       "  RowId                 Description Value Training Validation\n",
       "1 NREAD Number of Observations Read  5960     4172       1788\n",
       "2 NUSED Number of Observations Used  5960     4172       1788\n",
       "\n",
       "$ResponseProfile\n",
       "  OrderedValue Outcome BAD Freq Training Validation Modeled\n",
       "1            1       0   0 4771     3331       1440       *\n",
       "2            2       1   1 1189      841        348        \n",
       "\n",
       "$ClassInfo\n",
       "       Class Levels                              Values\n",
       "1    IMP_JOB      6 Mgr Office Other ProfExe Sales Self\n",
       "2 IMP_REASON      2                     DebtCon HomeImp\n",
       "\n",
       "$SelectionInfo\n",
       "          RowId               Description    Value NValue\n",
       "1        METHOD          Selection Method Backward    NaN\n",
       "2  SELCRITERION          Select Criterion      SBC    NaN\n",
       "3 STOPCRITERION            Stop Criterion      SBC    NaN\n",
       "4     HIERARCHY Effect Hierarchy Enforced   Single    NaN\n",
       "5   STOPHORIZON              Stop Horizon        3      3\n",
       "\n",
       "$Summary.ConvergenceStatus\n",
       "                                         Reason Status  MaxGradient\n",
       "1 Convergence criterion (FCONV=1E-7) satisfied.      0 0.0009467803\n",
       "\n",
       "$Summary.SelectionSummary\n",
       "  Control Step EffectRemoved nEffectsIn      SBC OptSBC\n",
       "1            0                       13 3363.055      0\n",
       "2       -    1       IMP_YOJ         12 3357.863      0\n",
       "3            2       IMP_JOB         11 3352.974      0\n",
       "4            3   IMP_MORTDUE         10 3349.044      1\n",
       "5            4    IMP_REASON          9 3349.604      0\n",
       "6            5      IMP_CLNO          8 3354.464      0\n",
       "7            6      IMP_LOAN          7 3372.209      0\n",
       "\n",
       "$Summary.StopReason\n",
       "                                                       Reason Code\n",
       "1 Selection stopped at a local minimum of the STOP criterion.    6\n",
       "\n",
       "$Summary.SelectionReason\n",
       "                            Reason\n",
       "1 The model at step 3 is selected.\n",
       "\n",
       "$Summary.SelectedEffects\n",
       "              Label\n",
       "1 Selected Effects:\n",
       "                                                                                               Effects\n",
       "1 Intercept IMP_CLAGE IMP_CLNO IMP_DEBTINC IMP_DELINQ IMP_DEROG IMP_LOAN IMP_NINQ IMP_VALUE IMP_REASON\n",
       "\n",
       "$SelectedModel.Dimensions\n",
       "        RowId                Description Value\n",
       "1 NDESIGNCOLS          Columns in Design    11\n",
       "2    NEFFECTS          Number of Effects    10\n",
       "3   MAXEFCOLS         Max Effect Columns     2\n",
       "4  DESIGNRANK             Rank of Design    10\n",
       "5     OPTPARM Parameters in Optimization    10\n",
       "\n",
       "$SelectedModel.GlobalTest\n",
       "              Test DF    ChiSq     ProbChiSq\n",
       "1 Likelihood Ratio  9 927.8438 6.187011e-194\n",
       "\n",
       "$SelectedModel.FitStatistics\n",
       "      RowId               Description     Training   Validation\n",
       "1      M2LL         -2 Log Likelihood 3265.7480145 1404.7441937\n",
       "2       AIC   AIC (smaller is better) 3285.7480145 1424.7441937\n",
       "3      AICC  AICC (smaller is better) 3285.8008864 1424.8679978\n",
       "4       SBC   SBC (smaller is better) 3349.1095227 1479.6327232\n",
       "5       ASE      Average Square Error    0.1208238    0.1208004\n",
       "6  M2LLNULL -2 Log L (Intercept-only) 4193.5917796 1762.4978732\n",
       "7   RSQUARE                  R-Square    0.1994032    0.1813396\n",
       "8    ADJRSQ     Max-rescaled R-Square    0.3145064    0.2892936\n",
       "9  MCFADDEN       McFadden's R-Square    0.2212528    0.2029811\n",
       "10 MISCLASS    Misclassification Rate    0.1598754    0.1610738\n",
       "11 DIFFMEAN       Difference of Means    0.2505009    0.2394360\n",
       "\n",
       "$SelectedModel.ParameterEstimates\n",
       "        Effect IMP_REASON          Parameter           ParmName DF\n",
       "1    Intercept                     Intercept          Intercept  1\n",
       "2    IMP_CLAGE                     IMP_CLAGE          IMP_CLAGE  1\n",
       "3     IMP_CLNO                      IMP_CLNO           IMP_CLNO  1\n",
       "4  IMP_DEBTINC                   IMP_DEBTINC        IMP_DEBTINC  1\n",
       "5   IMP_DELINQ                    IMP_DELINQ         IMP_DELINQ  1\n",
       "6    IMP_DEROG                     IMP_DEROG          IMP_DEROG  1\n",
       "7     IMP_LOAN                      IMP_LOAN           IMP_LOAN  1\n",
       "8     IMP_NINQ                      IMP_NINQ           IMP_NINQ  1\n",
       "9    IMP_VALUE                     IMP_VALUE          IMP_VALUE  1\n",
       "10  IMP_REASON    DebtCon IMP_REASON DebtCon IMP_REASON_DebtCon  1\n",
       "11  IMP_REASON    HomeImp IMP_REASON HomeImp IMP_REASON_HomeImp  0\n",
       "        Estimate       StdErr      ChiSq    ProbChiSq\n",
       "1   2.621205e+00 2.886291e-01  82.474890 1.070211e-19\n",
       "2   5.607892e-03 6.503098e-04  74.363313 6.498554e-18\n",
       "3   1.606460e-02 4.998012e-03  10.331073 1.308091e-03\n",
       "4  -6.249575e-02 7.415592e-03  71.024674 3.527853e-17\n",
       "5  -7.563151e-01 4.540623e-02 277.443533 2.708342e-62\n",
       "6  -6.573445e-01 5.903230e-02 123.995732 8.442498e-29\n",
       "7   2.135606e-05 4.873157e-06  19.205309 1.173865e-05\n",
       "8  -1.649363e-01 2.431238e-02  46.023296 1.168551e-11\n",
       "9  -1.258613e-06 9.087475e-07   1.918219 1.660531e-01\n",
       "10  2.940699e-01 9.895972e-02   8.830477 2.962409e-03\n",
       "11  0.000000e+00          NaN        NaN          NaN\n",
       "\n",
       "$Timing\n",
       "           RowId                 Task         Time     RelTime\n",
       "1          SETUP    Setup and Parsing 0.0409197807 0.068626965\n",
       "2   LEVELIZATION         Levelization 0.0499210358 0.083723058\n",
       "3 INITIALIZATION Model Initialization 0.0207328796 0.034771315\n",
       "4           SSCP     SSCP Computation 0.0463039875 0.077656871\n",
       "5        FITTING      Model Selection 0.3005890846 0.504120897\n",
       "6         OUTPUT Creating Output Data 0.0226120949 0.037922966\n",
       "7          STORE        Model Storing 0.1156239510 0.193914060\n",
       "8        CLEANUP              Cleanup 0.0008149147 0.001366701\n",
       "9          TOTAL                Total 0.5962638855 1.000000000\n",
       "\n",
       "$OutputCasTables\n",
       "              casLib      Name Label Rows Columns\n",
       "1 CASUSER(sasdemo05) log_model          1       2\n",
       "2 CASUSER(sasdemo05) log_score       5960       5\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build CAS Logistic Regression\n",
    "cas.regression.logistic(conn,\n",
    "    table = list(name = indata),\n",
    "    class = imp_nominals,\n",
    "    model = list(\n",
    "        depVar = target,\n",
    "        effects = imp_inputs),\n",
    "    selection=list(method=\"BACKWARD\"),\n",
    "    store=list(name='log_model', replace=TRUE), \n",
    "    output=list(casOut=list(name='log_score',replace=TRUE),  \n",
    "                pred='pred', resChi='reschi', into='into',\n",
    "                copyVars=list(target, '_PartInd_')), \n",
    "    partByVar= list(name = \"_partind_\", train = \"0\", validate = \"1\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Score Test Data\n",
    "log_score1 <- defCasTable(conn, tablename=\"log_score\")\n",
    "log_score1 = to.casDataFrame(log_score1)\n",
    "log_score1 = to.data.frame(log_score1)\n",
    "log_score1$pred1 = 1-log_score1$pred\n",
    "log_sc1 <- as.casTable(conn, log_score1, casOut='log_sc1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assess Performance\n",
    "log_assessed <- cas.percentile.assess(conn,\n",
    "                table = list(name = 'log_sc1', where = '_PartInd_ = 1'),\n",
    "                inputs = 'pred1',\n",
    "                response = target,\n",
    "                event = '1')\n",
    "log_roc <- log_assessed$ROCInfo\n",
    "log_roc$Model <- \"CAS Logistic Regression\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CAS Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$ModelInfo</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 11 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Descr</th><th scope=col>Value</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Number of Tree Nodes        </td><td>  17.00000</td></tr>\n",
       "\t<tr><td>Max Number of Branches      </td><td>   2.00000</td></tr>\n",
       "\t<tr><td>Number of Levels            </td><td>   6.00000</td></tr>\n",
       "\t<tr><td>Number of Leaves            </td><td>   9.00000</td></tr>\n",
       "\t<tr><td>Number of Bins              </td><td>  20.00000</td></tr>\n",
       "\t<tr><td>Minimum Size of Leaves      </td><td>   5.00000</td></tr>\n",
       "\t<tr><td>Maximum Size of Leaves      </td><td>3139.00000</td></tr>\n",
       "\t<tr><td>Number of Variables         </td><td>  24.00000</td></tr>\n",
       "\t<tr><td>Confidence Level for Pruning</td><td>   0.25000</td></tr>\n",
       "\t<tr><td>Number of Observations Used </td><td>4172.00000</td></tr>\n",
       "\t<tr><td>Misclassification Error (%) </td><td>  13.85427</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$DTreeVarImpInfo</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 5 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Variable</th><th scope=col>Importance</th><th scope=col>Std</th><th scope=col>Count</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>DEBTINC  </td><td>462.3418025</td><td>169.414308</td><td>3</td></tr>\n",
       "\t<tr><td>DELINQ   </td><td> 47.5949825</td><td>  0.000000</td><td>1</td></tr>\n",
       "\t<tr><td>CLNO     </td><td> 10.6693789</td><td>  3.277202</td><td>2</td></tr>\n",
       "\t<tr><td>DEROG    </td><td>  9.2955776</td><td>  0.000000</td><td>1</td></tr>\n",
       "\t<tr><td>IMP_VALUE</td><td>  0.3393939</td><td>  0.000000</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$OutputCasTables</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 1 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>casLib</th><th scope=col>Name</th><th scope=col>Rows</th><th scope=col>Columns</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>CASUSER(sasdemo05)</td><td>dt_model</td><td>17</td><td>27</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$ModelInfo] A casDataFrame: 11 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " Descr & Value\\\\\n",
       " <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Number of Tree Nodes         &   17.00000\\\\\n",
       "\t Max Number of Branches       &    2.00000\\\\\n",
       "\t Number of Levels             &    6.00000\\\\\n",
       "\t Number of Leaves             &    9.00000\\\\\n",
       "\t Number of Bins               &   20.00000\\\\\n",
       "\t Minimum Size of Leaves       &    5.00000\\\\\n",
       "\t Maximum Size of Leaves       & 3139.00000\\\\\n",
       "\t Number of Variables          &   24.00000\\\\\n",
       "\t Confidence Level for Pruning &    0.25000\\\\\n",
       "\t Number of Observations Used  & 4172.00000\\\\\n",
       "\t Misclassification Error (\\%)  &   13.85427\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$DTreeVarImpInfo] A casDataFrame: 5 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " Variable & Importance & Std & Count\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t DEBTINC   & 462.3418025 & 169.414308 & 3\\\\\n",
       "\t DELINQ    &  47.5949825 &   0.000000 & 1\\\\\n",
       "\t CLNO      &  10.6693789 &   3.277202 & 2\\\\\n",
       "\t DEROG     &   9.2955776 &   0.000000 & 1\\\\\n",
       "\t IMP\\_VALUE &   0.3393939 &   0.000000 & 1\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$OutputCasTables] A casDataFrame: 1 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " casLib & Name & Rows & Columns\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t CASUSER(sasdemo05) & dt\\_model & 17 & 27\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$ModelInfo\n",
       ":   \n",
       "A casDataFrame: 11 × 2\n",
       "\n",
       "| Descr &lt;chr&gt; | Value &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| Number of Tree Nodes         |   17.00000 |\n",
       "| Max Number of Branches       |    2.00000 |\n",
       "| Number of Levels             |    6.00000 |\n",
       "| Number of Leaves             |    9.00000 |\n",
       "| Number of Bins               |   20.00000 |\n",
       "| Minimum Size of Leaves       |    5.00000 |\n",
       "| Maximum Size of Leaves       | 3139.00000 |\n",
       "| Number of Variables          |   24.00000 |\n",
       "| Confidence Level for Pruning |    0.25000 |\n",
       "| Number of Observations Used  | 4172.00000 |\n",
       "| Misclassification Error (%)  |   13.85427 |\n",
       "\n",
       "\n",
       "$DTreeVarImpInfo\n",
       ":   \n",
       "A casDataFrame: 5 × 4\n",
       "\n",
       "| Variable &lt;chr&gt; | Importance &lt;dbl&gt; | Std &lt;dbl&gt; | Count &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| DEBTINC   | 462.3418025 | 169.414308 | 3 |\n",
       "| DELINQ    |  47.5949825 |   0.000000 | 1 |\n",
       "| CLNO      |  10.6693789 |   3.277202 | 2 |\n",
       "| DEROG     |   9.2955776 |   0.000000 | 1 |\n",
       "| IMP_VALUE |   0.3393939 |   0.000000 | 1 |\n",
       "\n",
       "\n",
       "$OutputCasTables\n",
       ":   \n",
       "A casDataFrame: 1 × 4\n",
       "\n",
       "| casLib &lt;chr&gt; | Name &lt;chr&gt; | Rows &lt;dbl&gt; | Columns &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| CASUSER(sasdemo05) | dt_model | 17 | 27 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$ModelInfo\n",
       "                          Descr      Value\n",
       "1          Number of Tree Nodes   17.00000\n",
       "2        Max Number of Branches    2.00000\n",
       "3              Number of Levels    6.00000\n",
       "4              Number of Leaves    9.00000\n",
       "5                Number of Bins   20.00000\n",
       "6        Minimum Size of Leaves    5.00000\n",
       "7        Maximum Size of Leaves 3139.00000\n",
       "8           Number of Variables   24.00000\n",
       "9  Confidence Level for Pruning    0.25000\n",
       "10  Number of Observations Used 4172.00000\n",
       "11  Misclassification Error (%)   13.85427\n",
       "\n",
       "$DTreeVarImpInfo\n",
       "   Variable  Importance        Std Count\n",
       "1   DEBTINC 462.3418025 169.414308     3\n",
       "2    DELINQ  47.5949825   0.000000     1\n",
       "3      CLNO  10.6693789   3.277202     2\n",
       "4     DEROG   9.2955776   0.000000     1\n",
       "5 IMP_VALUE   0.3393939   0.000000     1\n",
       "\n",
       "$OutputCasTables\n",
       "              casLib     Name Rows Columns\n",
       "1 CASUSER(sasdemo05) dt_model   17      27\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build CAS Decision Tree\n",
    "cas.decisionTree.dtreeTrain(conn,\n",
    "    table = list(name = indata, where = '_PartInd_ = 0'),\n",
    "    target = target,\n",
    "    inputs = inputs,\n",
    "    nominals = nominals,\n",
    "    varImp = TRUE,\n",
    "    casOut = list(name = 'dt_model', replace = TRUE)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$OutputCasTables</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 1 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>casLib</th><th scope=col>Name</th><th scope=col>Rows</th><th scope=col>Columns</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>CASUSER(sasdemo05)</td><td>dt_scored</td><td>5960</td><td>16</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$ScoreInfo</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 3 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Descr</th><th scope=col>Value</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Number of Observations Read</td><td>                            5960</td></tr>\n",
       "\t<tr><td>Number of Observations Used</td><td>                            5960</td></tr>\n",
       "\t<tr><td>Misclassification Error (%)</td><td>                    13.674496644</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$OutputCasTables] A casDataFrame: 1 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " casLib & Name & Rows & Columns\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t CASUSER(sasdemo05) & dt\\_scored & 5960 & 16\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$ScoreInfo] A casDataFrame: 3 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " Descr & Value\\\\\n",
       " <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Number of Observations Read &                             5960\\\\\n",
       "\t Number of Observations Used &                             5960\\\\\n",
       "\t Misclassification Error (\\%) &                     13.674496644\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$OutputCasTables\n",
       ":   \n",
       "A casDataFrame: 1 × 4\n",
       "\n",
       "| casLib &lt;chr&gt; | Name &lt;chr&gt; | Rows &lt;dbl&gt; | Columns &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| CASUSER(sasdemo05) | dt_scored | 5960 | 16 |\n",
       "\n",
       "\n",
       "$ScoreInfo\n",
       ":   \n",
       "A casDataFrame: 3 × 2\n",
       "\n",
       "| Descr &lt;chr&gt; | Value &lt;chr&gt; |\n",
       "|---|---|\n",
       "| Number of Observations Read |                             5960 |\n",
       "| Number of Observations Used |                             5960 |\n",
       "| Misclassification Error (%) |                     13.674496644 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$OutputCasTables\n",
       "              casLib      Name Rows Columns\n",
       "1 CASUSER(sasdemo05) dt_scored 5960      16\n",
       "\n",
       "$ScoreInfo\n",
       "                        Descr                            Value\n",
       "1 Number of Observations Read                             5960\n",
       "2 Number of Observations Used                             5960\n",
       "3 Misclassification Error (%)                     13.674496644\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Score Test Data \n",
    "cas.decisionTree.dtreeScore(conn,\n",
    "    modelTable=list(name='dt_model'),\n",
    "    table=list(name=indata),\n",
    "    copyVars= list(target, '_PartInd_'),  \n",
    "    assessOneRow=TRUE, \n",
    "    casOut = list(name = 'dt_scored', replace = T)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assess Performance \n",
    "dt_assessed <- cas.percentile.assess(conn,\n",
    "                table = list(name = 'dt_scored', where = '_PartInd_ = 1'),\n",
    "                inputs = '_DT_P_           1',\n",
    "                response = target,\n",
    "                event = '1')\n",
    "dt_roc <- dt_assessed$ROCInfo\n",
    "dt_roc$Model <- \"CAS Decision Tree\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CAS Autotuned Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find Best Decision Tree Configuration\n",
    "tune_dt <- cas.autotune.tuneDecisionTree(conn, \n",
    "            trainOptions=list(table = list(name = indata, where = '_PartInd_ = 0'),\n",
    "            target = target,\n",
    "            inputs = inputs,\n",
    "            nominals = nominals,\n",
    "            varImp = TRUE,\n",
    "            casOut = list(name = 'tune_dt_model', replace = TRUE)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$OutputCasTables</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 1 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>casLib</th><th scope=col>Name</th><th scope=col>Rows</th><th scope=col>Columns</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>CASUSER(sasdemo05)</td><td>tune_dt_scored</td><td>5960</td><td>16</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$ScoreInfo</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 3 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Descr</th><th scope=col>Value</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Number of Observations Read</td><td>                            5960</td></tr>\n",
       "\t<tr><td>Number of Observations Used</td><td>                            5960</td></tr>\n",
       "\t<tr><td>Misclassification Error (%)</td><td>                    10.822147651</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$OutputCasTables] A casDataFrame: 1 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " casLib & Name & Rows & Columns\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t CASUSER(sasdemo05) & tune\\_dt\\_scored & 5960 & 16\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$ScoreInfo] A casDataFrame: 3 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " Descr & Value\\\\\n",
       " <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Number of Observations Read &                             5960\\\\\n",
       "\t Number of Observations Used &                             5960\\\\\n",
       "\t Misclassification Error (\\%) &                     10.822147651\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$OutputCasTables\n",
       ":   \n",
       "A casDataFrame: 1 × 4\n",
       "\n",
       "| casLib &lt;chr&gt; | Name &lt;chr&gt; | Rows &lt;dbl&gt; | Columns &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| CASUSER(sasdemo05) | tune_dt_scored | 5960 | 16 |\n",
       "\n",
       "\n",
       "$ScoreInfo\n",
       ":   \n",
       "A casDataFrame: 3 × 2\n",
       "\n",
       "| Descr &lt;chr&gt; | Value &lt;chr&gt; |\n",
       "|---|---|\n",
       "| Number of Observations Read |                             5960 |\n",
       "| Number of Observations Used |                             5960 |\n",
       "| Misclassification Error (%) |                     10.822147651 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$OutputCasTables\n",
       "              casLib           Name Rows Columns\n",
       "1 CASUSER(sasdemo05) tune_dt_scored 5960      16\n",
       "\n",
       "$ScoreInfo\n",
       "                        Descr                            Value\n",
       "1 Number of Observations Read                             5960\n",
       "2 Number of Observations Used                             5960\n",
       "3 Misclassification Error (%)                     10.822147651\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Score Test Data \n",
    "cas.decisionTree.dtreeScore(conn,\n",
    "    table=list(name = indata),\n",
    "    modelTable='tune_dt_model',\n",
    "    copyVars     = list(target, '_PartInd_'),      \n",
    "    assessonerow = TRUE,\n",
    "    casOut       = list(name = 'tune_dt_scored', replace = T)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assess Performance \n",
    "tune_dt_assessed <- cas.percentile.assess(conn,\n",
    "                table = list(name = 'tune_dt_scored', where = '_PartInd_ = 1'),\n",
    "                inputs = '_DT_P_           1',\n",
    "                response = target,\n",
    "                event = '1')\n",
    "tune_dt_roc <- tune_dt_assessed$ROCInfo\n",
    "tune_dt_roc$Model <- \"CAS Autotuned Decision Tree\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CAS Gradient Boosting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$ModelInfo</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 18 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Descr</th><th scope=col>Value</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Number of Trees                 </td><td>  50.0</td></tr>\n",
       "\t<tr><td>Distribution                    </td><td>   2.0</td></tr>\n",
       "\t<tr><td>Learning Rate                   </td><td>   0.1</td></tr>\n",
       "\t<tr><td>Subsampling Rate                </td><td>   0.5</td></tr>\n",
       "\t<tr><td>Number of Selected Variables (M)</td><td>  24.0</td></tr>\n",
       "\t<tr><td>Number of Bins                  </td><td>  50.0</td></tr>\n",
       "\t<tr><td>Number of Variables             </td><td>  24.0</td></tr>\n",
       "\t<tr><td>Max Number of Tree Nodes        </td><td>  31.0</td></tr>\n",
       "\t<tr><td>Min Number of Tree Nodes        </td><td>  21.0</td></tr>\n",
       "\t<tr><td>Max Number of Branches          </td><td>   2.0</td></tr>\n",
       "\t<tr><td>Min Number of Branches          </td><td>   2.0</td></tr>\n",
       "\t<tr><td>Max Number of Levels            </td><td>   5.0</td></tr>\n",
       "\t<tr><td>Min Number of Levels            </td><td>   5.0</td></tr>\n",
       "\t<tr><td>Max Number of Leaves            </td><td>  16.0</td></tr>\n",
       "\t<tr><td>Min Number of Leaves            </td><td>  11.0</td></tr>\n",
       "\t<tr><td>Maximum Size of Leaves          </td><td>1895.0</td></tr>\n",
       "\t<tr><td>Minimum Size of Leaves          </td><td>   5.0</td></tr>\n",
       "\t<tr><td>Random Number Seed              </td><td>   0.0</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$OutputCasTables</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 1 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>casLib</th><th scope=col>Name</th><th scope=col>Rows</th><th scope=col>Columns</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>CASUSER(sasdemo05)</td><td>gbt_model</td><td>1392</td><td>32</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$ModelInfo] A casDataFrame: 18 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " Descr & Value\\\\\n",
       " <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Number of Trees                  &   50.0\\\\\n",
       "\t Distribution                     &    2.0\\\\\n",
       "\t Learning Rate                    &    0.1\\\\\n",
       "\t Subsampling Rate                 &    0.5\\\\\n",
       "\t Number of Selected Variables (M) &   24.0\\\\\n",
       "\t Number of Bins                   &   50.0\\\\\n",
       "\t Number of Variables              &   24.0\\\\\n",
       "\t Max Number of Tree Nodes         &   31.0\\\\\n",
       "\t Min Number of Tree Nodes         &   21.0\\\\\n",
       "\t Max Number of Branches           &    2.0\\\\\n",
       "\t Min Number of Branches           &    2.0\\\\\n",
       "\t Max Number of Levels             &    5.0\\\\\n",
       "\t Min Number of Levels             &    5.0\\\\\n",
       "\t Max Number of Leaves             &   16.0\\\\\n",
       "\t Min Number of Leaves             &   11.0\\\\\n",
       "\t Maximum Size of Leaves           & 1895.0\\\\\n",
       "\t Minimum Size of Leaves           &    5.0\\\\\n",
       "\t Random Number Seed               &    0.0\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$OutputCasTables] A casDataFrame: 1 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " casLib & Name & Rows & Columns\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t CASUSER(sasdemo05) & gbt\\_model & 1392 & 32\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$ModelInfo\n",
       ":   \n",
       "A casDataFrame: 18 × 2\n",
       "\n",
       "| Descr &lt;chr&gt; | Value &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| Number of Trees                  |   50.0 |\n",
       "| Distribution                     |    2.0 |\n",
       "| Learning Rate                    |    0.1 |\n",
       "| Subsampling Rate                 |    0.5 |\n",
       "| Number of Selected Variables (M) |   24.0 |\n",
       "| Number of Bins                   |   50.0 |\n",
       "| Number of Variables              |   24.0 |\n",
       "| Max Number of Tree Nodes         |   31.0 |\n",
       "| Min Number of Tree Nodes         |   21.0 |\n",
       "| Max Number of Branches           |    2.0 |\n",
       "| Min Number of Branches           |    2.0 |\n",
       "| Max Number of Levels             |    5.0 |\n",
       "| Min Number of Levels             |    5.0 |\n",
       "| Max Number of Leaves             |   16.0 |\n",
       "| Min Number of Leaves             |   11.0 |\n",
       "| Maximum Size of Leaves           | 1895.0 |\n",
       "| Minimum Size of Leaves           |    5.0 |\n",
       "| Random Number Seed               |    0.0 |\n",
       "\n",
       "\n",
       "$OutputCasTables\n",
       ":   \n",
       "A casDataFrame: 1 × 4\n",
       "\n",
       "| casLib &lt;chr&gt; | Name &lt;chr&gt; | Rows &lt;dbl&gt; | Columns &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| CASUSER(sasdemo05) | gbt_model | 1392 | 32 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$ModelInfo\n",
       "                              Descr  Value\n",
       "1                   Number of Trees   50.0\n",
       "2                      Distribution    2.0\n",
       "3                     Learning Rate    0.1\n",
       "4                  Subsampling Rate    0.5\n",
       "5  Number of Selected Variables (M)   24.0\n",
       "6                    Number of Bins   50.0\n",
       "7               Number of Variables   24.0\n",
       "8          Max Number of Tree Nodes   31.0\n",
       "9          Min Number of Tree Nodes   21.0\n",
       "10           Max Number of Branches    2.0\n",
       "11           Min Number of Branches    2.0\n",
       "12             Max Number of Levels    5.0\n",
       "13             Min Number of Levels    5.0\n",
       "14             Max Number of Leaves   16.0\n",
       "15             Min Number of Leaves   11.0\n",
       "16           Maximum Size of Leaves 1895.0\n",
       "17           Minimum Size of Leaves    5.0\n",
       "18               Random Number Seed    0.0\n",
       "\n",
       "$OutputCasTables\n",
       "              casLib      Name Rows Columns\n",
       "1 CASUSER(sasdemo05) gbt_model 1392      32\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gradient Boosting\n",
    "cas.decisionTree.gbtreeTrain(conn,\n",
    "    table = list(name = indata, where = '_PartInd_ = 0'),\n",
    "    target = target,\n",
    "    inputs = inputs,\n",
    "    nominals = nominals,\n",
    "    casOut = list(name = 'gbt_model', replace = TRUE)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$OutputCasTables</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 1 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>casLib</th><th scope=col>Name</th><th scope=col>Rows</th><th scope=col>Columns</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>CASUSER(sasdemo05)</td><td>gbt_scored</td><td>5960</td><td>8</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$ScoreInfo</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 3 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Descr</th><th scope=col>Value</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Number of Observations Read</td><td>                            5960</td></tr>\n",
       "\t<tr><td>Number of Observations Used</td><td>                            5960</td></tr>\n",
       "\t<tr><td>Misclassification Error (%)</td><td>                    8.4731543624</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$ErrorMetricInfo</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A casDataFrame: 50 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>TreeID</th><th scope=col>Trees</th><th scope=col>NLeaves</th><th scope=col>MCR</th><th scope=col>LogLoss</th><th scope=col>ASE</th><th scope=col>RASE</th><th scope=col>MAXAE</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td> 0</td><td> 1</td><td> 13</td><td>0.19949664</td><td>0.4583175</td><td>0.14542261</td><td>0.3813432</td><td>0.8162591</td></tr>\n",
       "\t<tr><td> 1</td><td> 2</td><td> 28</td><td>0.19949664</td><td>0.4289321</td><td>0.13438190</td><td>0.3665814</td><td>0.8296554</td></tr>\n",
       "\t<tr><td> 2</td><td> 3</td><td> 44</td><td>0.19949664</td><td>0.4050840</td><td>0.12522465</td><td>0.3538709</td><td>0.8428032</td></tr>\n",
       "\t<tr><td> 3</td><td> 4</td><td> 60</td><td>0.19949664</td><td>0.3859273</td><td>0.11772208</td><td>0.3431065</td><td>0.8550105</td></tr>\n",
       "\t<tr><td> 4</td><td> 5</td><td> 74</td><td>0.19949664</td><td>0.3704777</td><td>0.11181257</td><td>0.3343839</td><td>0.8662366</td></tr>\n",
       "\t<tr><td> 5</td><td> 6</td><td> 90</td><td>0.17466443</td><td>0.3562004</td><td>0.10643394</td><td>0.3262421</td><td>0.8759018</td></tr>\n",
       "\t<tr><td> 6</td><td> 7</td><td>106</td><td>0.14530201</td><td>0.3442756</td><td>0.10189294</td><td>0.3192067</td><td>0.8837723</td></tr>\n",
       "\t<tr><td> 7</td><td> 8</td><td>120</td><td>0.13322148</td><td>0.3340708</td><td>0.09815133</td><td>0.3132911</td><td>0.8926982</td></tr>\n",
       "\t<tr><td> 8</td><td> 9</td><td>135</td><td>0.11879195</td><td>0.3250084</td><td>0.09485241</td><td>0.3079812</td><td>0.9000767</td></tr>\n",
       "\t<tr><td> 9</td><td>10</td><td>150</td><td>0.11325503</td><td>0.3166343</td><td>0.09196214</td><td>0.3032526</td><td>0.9064096</td></tr>\n",
       "\t<tr><td>10</td><td>11</td><td>165</td><td>0.10989933</td><td>0.3102472</td><td>0.08982883</td><td>0.2997146</td><td>0.9124400</td></tr>\n",
       "\t<tr><td>11</td><td>12</td><td>181</td><td>0.10704698</td><td>0.3033679</td><td>0.08753238</td><td>0.2958587</td><td>0.9194278</td></tr>\n",
       "\t<tr><td>12</td><td>13</td><td>195</td><td>0.10604027</td><td>0.2976786</td><td>0.08576177</td><td>0.2928511</td><td>0.9261137</td></tr>\n",
       "\t<tr><td>13</td><td>14</td><td>211</td><td>0.10520134</td><td>0.2931048</td><td>0.08436454</td><td>0.2904557</td><td>0.9314005</td></tr>\n",
       "\t<tr><td>14</td><td>15</td><td>225</td><td>0.10436242</td><td>0.2883862</td><td>0.08292363</td><td>0.2879646</td><td>0.9353805</td></tr>\n",
       "\t<tr><td>15</td><td>16</td><td>241</td><td>0.10218121</td><td>0.2844676</td><td>0.08184719</td><td>0.2860895</td><td>0.9406621</td></tr>\n",
       "\t<tr><td>16</td><td>17</td><td>255</td><td>0.10285235</td><td>0.2802390</td><td>0.08068540</td><td>0.2840518</td><td>0.9449353</td></tr>\n",
       "\t<tr><td>17</td><td>18</td><td>271</td><td>0.10251678</td><td>0.2768595</td><td>0.07969209</td><td>0.2822979</td><td>0.9489485</td></tr>\n",
       "\t<tr><td>18</td><td>19</td><td>287</td><td>0.10134228</td><td>0.2736584</td><td>0.07881852</td><td>0.2807464</td><td>0.9531432</td></tr>\n",
       "\t<tr><td>19</td><td>20</td><td>301</td><td>0.10100671</td><td>0.2708183</td><td>0.07803602</td><td>0.2793493</td><td>0.9569792</td></tr>\n",
       "\t<tr><td>20</td><td>21</td><td>313</td><td>0.10134228</td><td>0.2678495</td><td>0.07706816</td><td>0.2776115</td><td>0.9583204</td></tr>\n",
       "\t<tr><td>21</td><td>22</td><td>327</td><td>0.10016779</td><td>0.2650589</td><td>0.07624769</td><td>0.2761298</td><td>0.9614470</td></tr>\n",
       "\t<tr><td>22</td><td>23</td><td>342</td><td>0.10000000</td><td>0.2622113</td><td>0.07547845</td><td>0.2747334</td><td>0.9632765</td></tr>\n",
       "\t<tr><td>23</td><td>24</td><td>358</td><td>0.09865772</td><td>0.2589126</td><td>0.07445794</td><td>0.2728698</td><td>0.9643662</td></tr>\n",
       "\t<tr><td>24</td><td>25</td><td>372</td><td>0.09899329</td><td>0.2565501</td><td>0.07374377</td><td>0.2715580</td><td>0.9657733</td></tr>\n",
       "\t<tr><td>25</td><td>26</td><td>385</td><td>0.09781879</td><td>0.2544412</td><td>0.07315839</td><td>0.2704781</td><td>0.9670358</td></tr>\n",
       "\t<tr><td>26</td><td>27</td><td>398</td><td>0.09748322</td><td>0.2525427</td><td>0.07267208</td><td>0.2695776</td><td>0.9686246</td></tr>\n",
       "\t<tr><td>27</td><td>28</td><td>412</td><td>0.09714765</td><td>0.2503599</td><td>0.07200030</td><td>0.2683287</td><td>0.9691852</td></tr>\n",
       "\t<tr><td>28</td><td>29</td><td>426</td><td>0.09714765</td><td>0.2481523</td><td>0.07135334</td><td>0.2671205</td><td>0.9699781</td></tr>\n",
       "\t<tr><td>29</td><td>30</td><td>440</td><td>0.09580537</td><td>0.2458725</td><td>0.07065265</td><td>0.2658057</td><td>0.9714686</td></tr>\n",
       "\t<tr><td>30</td><td>31</td><td>456</td><td>0.09446309</td><td>0.2440846</td><td>0.07018587</td><td>0.2649262</td><td>0.9727930</td></tr>\n",
       "\t<tr><td>31</td><td>32</td><td>470</td><td>0.09412752</td><td>0.2422631</td><td>0.06960415</td><td>0.2638260</td><td>0.9735549</td></tr>\n",
       "\t<tr><td>32</td><td>33</td><td>483</td><td>0.09345638</td><td>0.2403532</td><td>0.06903100</td><td>0.2627375</td><td>0.9742737</td></tr>\n",
       "\t<tr><td>33</td><td>34</td><td>494</td><td>0.09328859</td><td>0.2387300</td><td>0.06855810</td><td>0.2618360</td><td>0.9745890</td></tr>\n",
       "\t<tr><td>34</td><td>35</td><td>508</td><td>0.09295302</td><td>0.2372782</td><td>0.06811972</td><td>0.2609976</td><td>0.9756157</td></tr>\n",
       "\t<tr><td>35</td><td>36</td><td>523</td><td>0.09211409</td><td>0.2352888</td><td>0.06756168</td><td>0.2599263</td><td>0.9741923</td></tr>\n",
       "\t<tr><td>36</td><td>37</td><td>538</td><td>0.09060403</td><td>0.2328913</td><td>0.06689158</td><td>0.2586341</td><td>0.9759766</td></tr>\n",
       "\t<tr><td>37</td><td>38</td><td>550</td><td>0.08959732</td><td>0.2316356</td><td>0.06652793</td><td>0.2579301</td><td>0.9780755</td></tr>\n",
       "\t<tr><td>38</td><td>39</td><td>563</td><td>0.08976510</td><td>0.2303145</td><td>0.06613225</td><td>0.2571619</td><td>0.9791260</td></tr>\n",
       "\t<tr><td>39</td><td>40</td><td>579</td><td>0.08926174</td><td>0.2278412</td><td>0.06535282</td><td>0.2556420</td><td>0.9794762</td></tr>\n",
       "\t<tr><td>40</td><td>41</td><td>591</td><td>0.08909396</td><td>0.2265814</td><td>0.06492543</td><td>0.2548047</td><td>0.9786891</td></tr>\n",
       "\t<tr><td>41</td><td>42</td><td>603</td><td>0.08842282</td><td>0.2256198</td><td>0.06464658</td><td>0.2542569</td><td>0.9789643</td></tr>\n",
       "\t<tr><td>42</td><td>43</td><td>617</td><td>0.08875839</td><td>0.2243108</td><td>0.06423675</td><td>0.2534497</td><td>0.9790753</td></tr>\n",
       "\t<tr><td>43</td><td>44</td><td>632</td><td>0.08892617</td><td>0.2230472</td><td>0.06386086</td><td>0.2527071</td><td>0.9801133</td></tr>\n",
       "\t<tr><td>44</td><td>45</td><td>648</td><td>0.08775168</td><td>0.2213631</td><td>0.06345153</td><td>0.2518959</td><td>0.9817142</td></tr>\n",
       "\t<tr><td>45</td><td>46</td><td>664</td><td>0.08708054</td><td>0.2200668</td><td>0.06308301</td><td>0.2511633</td><td>0.9822180</td></tr>\n",
       "\t<tr><td>46</td><td>47</td><td>680</td><td>0.08691275</td><td>0.2183415</td><td>0.06257704</td><td>0.2501540</td><td>0.9829572</td></tr>\n",
       "\t<tr><td>47</td><td>48</td><td>696</td><td>0.08573826</td><td>0.2169110</td><td>0.06213175</td><td>0.2492624</td><td>0.9836021</td></tr>\n",
       "\t<tr><td>48</td><td>49</td><td>707</td><td>0.08540268</td><td>0.2161872</td><td>0.06188125</td><td>0.2487594</td><td>0.9836874</td></tr>\n",
       "\t<tr><td>49</td><td>50</td><td>721</td><td>0.08473154</td><td>0.2151943</td><td>0.06158102</td><td>0.2481552</td><td>0.9847375</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$OutputCasTables] A casDataFrame: 1 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " casLib & Name & Rows & Columns\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t CASUSER(sasdemo05) & gbt\\_scored & 5960 & 8\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$ScoreInfo] A casDataFrame: 3 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " Descr & Value\\\\\n",
       " <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Number of Observations Read &                             5960\\\\\n",
       "\t Number of Observations Used &                             5960\\\\\n",
       "\t Misclassification Error (\\%) &                     8.4731543624\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$ErrorMetricInfo] A casDataFrame: 50 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       " TreeID & Trees & NLeaves & MCR & LogLoss & ASE & RASE & MAXAE\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t  0 &  1 &  13 & 0.19949664 & 0.4583175 & 0.14542261 & 0.3813432 & 0.8162591\\\\\n",
       "\t  1 &  2 &  28 & 0.19949664 & 0.4289321 & 0.13438190 & 0.3665814 & 0.8296554\\\\\n",
       "\t  2 &  3 &  44 & 0.19949664 & 0.4050840 & 0.12522465 & 0.3538709 & 0.8428032\\\\\n",
       "\t  3 &  4 &  60 & 0.19949664 & 0.3859273 & 0.11772208 & 0.3431065 & 0.8550105\\\\\n",
       "\t  4 &  5 &  74 & 0.19949664 & 0.3704777 & 0.11181257 & 0.3343839 & 0.8662366\\\\\n",
       "\t  5 &  6 &  90 & 0.17466443 & 0.3562004 & 0.10643394 & 0.3262421 & 0.8759018\\\\\n",
       "\t  6 &  7 & 106 & 0.14530201 & 0.3442756 & 0.10189294 & 0.3192067 & 0.8837723\\\\\n",
       "\t  7 &  8 & 120 & 0.13322148 & 0.3340708 & 0.09815133 & 0.3132911 & 0.8926982\\\\\n",
       "\t  8 &  9 & 135 & 0.11879195 & 0.3250084 & 0.09485241 & 0.3079812 & 0.9000767\\\\\n",
       "\t  9 & 10 & 150 & 0.11325503 & 0.3166343 & 0.09196214 & 0.3032526 & 0.9064096\\\\\n",
       "\t 10 & 11 & 165 & 0.10989933 & 0.3102472 & 0.08982883 & 0.2997146 & 0.9124400\\\\\n",
       "\t 11 & 12 & 181 & 0.10704698 & 0.3033679 & 0.08753238 & 0.2958587 & 0.9194278\\\\\n",
       "\t 12 & 13 & 195 & 0.10604027 & 0.2976786 & 0.08576177 & 0.2928511 & 0.9261137\\\\\n",
       "\t 13 & 14 & 211 & 0.10520134 & 0.2931048 & 0.08436454 & 0.2904557 & 0.9314005\\\\\n",
       "\t 14 & 15 & 225 & 0.10436242 & 0.2883862 & 0.08292363 & 0.2879646 & 0.9353805\\\\\n",
       "\t 15 & 16 & 241 & 0.10218121 & 0.2844676 & 0.08184719 & 0.2860895 & 0.9406621\\\\\n",
       "\t 16 & 17 & 255 & 0.10285235 & 0.2802390 & 0.08068540 & 0.2840518 & 0.9449353\\\\\n",
       "\t 17 & 18 & 271 & 0.10251678 & 0.2768595 & 0.07969209 & 0.2822979 & 0.9489485\\\\\n",
       "\t 18 & 19 & 287 & 0.10134228 & 0.2736584 & 0.07881852 & 0.2807464 & 0.9531432\\\\\n",
       "\t 19 & 20 & 301 & 0.10100671 & 0.2708183 & 0.07803602 & 0.2793493 & 0.9569792\\\\\n",
       "\t 20 & 21 & 313 & 0.10134228 & 0.2678495 & 0.07706816 & 0.2776115 & 0.9583204\\\\\n",
       "\t 21 & 22 & 327 & 0.10016779 & 0.2650589 & 0.07624769 & 0.2761298 & 0.9614470\\\\\n",
       "\t 22 & 23 & 342 & 0.10000000 & 0.2622113 & 0.07547845 & 0.2747334 & 0.9632765\\\\\n",
       "\t 23 & 24 & 358 & 0.09865772 & 0.2589126 & 0.07445794 & 0.2728698 & 0.9643662\\\\\n",
       "\t 24 & 25 & 372 & 0.09899329 & 0.2565501 & 0.07374377 & 0.2715580 & 0.9657733\\\\\n",
       "\t 25 & 26 & 385 & 0.09781879 & 0.2544412 & 0.07315839 & 0.2704781 & 0.9670358\\\\\n",
       "\t 26 & 27 & 398 & 0.09748322 & 0.2525427 & 0.07267208 & 0.2695776 & 0.9686246\\\\\n",
       "\t 27 & 28 & 412 & 0.09714765 & 0.2503599 & 0.07200030 & 0.2683287 & 0.9691852\\\\\n",
       "\t 28 & 29 & 426 & 0.09714765 & 0.2481523 & 0.07135334 & 0.2671205 & 0.9699781\\\\\n",
       "\t 29 & 30 & 440 & 0.09580537 & 0.2458725 & 0.07065265 & 0.2658057 & 0.9714686\\\\\n",
       "\t 30 & 31 & 456 & 0.09446309 & 0.2440846 & 0.07018587 & 0.2649262 & 0.9727930\\\\\n",
       "\t 31 & 32 & 470 & 0.09412752 & 0.2422631 & 0.06960415 & 0.2638260 & 0.9735549\\\\\n",
       "\t 32 & 33 & 483 & 0.09345638 & 0.2403532 & 0.06903100 & 0.2627375 & 0.9742737\\\\\n",
       "\t 33 & 34 & 494 & 0.09328859 & 0.2387300 & 0.06855810 & 0.2618360 & 0.9745890\\\\\n",
       "\t 34 & 35 & 508 & 0.09295302 & 0.2372782 & 0.06811972 & 0.2609976 & 0.9756157\\\\\n",
       "\t 35 & 36 & 523 & 0.09211409 & 0.2352888 & 0.06756168 & 0.2599263 & 0.9741923\\\\\n",
       "\t 36 & 37 & 538 & 0.09060403 & 0.2328913 & 0.06689158 & 0.2586341 & 0.9759766\\\\\n",
       "\t 37 & 38 & 550 & 0.08959732 & 0.2316356 & 0.06652793 & 0.2579301 & 0.9780755\\\\\n",
       "\t 38 & 39 & 563 & 0.08976510 & 0.2303145 & 0.06613225 & 0.2571619 & 0.9791260\\\\\n",
       "\t 39 & 40 & 579 & 0.08926174 & 0.2278412 & 0.06535282 & 0.2556420 & 0.9794762\\\\\n",
       "\t 40 & 41 & 591 & 0.08909396 & 0.2265814 & 0.06492543 & 0.2548047 & 0.9786891\\\\\n",
       "\t 41 & 42 & 603 & 0.08842282 & 0.2256198 & 0.06464658 & 0.2542569 & 0.9789643\\\\\n",
       "\t 42 & 43 & 617 & 0.08875839 & 0.2243108 & 0.06423675 & 0.2534497 & 0.9790753\\\\\n",
       "\t 43 & 44 & 632 & 0.08892617 & 0.2230472 & 0.06386086 & 0.2527071 & 0.9801133\\\\\n",
       "\t 44 & 45 & 648 & 0.08775168 & 0.2213631 & 0.06345153 & 0.2518959 & 0.9817142\\\\\n",
       "\t 45 & 46 & 664 & 0.08708054 & 0.2200668 & 0.06308301 & 0.2511633 & 0.9822180\\\\\n",
       "\t 46 & 47 & 680 & 0.08691275 & 0.2183415 & 0.06257704 & 0.2501540 & 0.9829572\\\\\n",
       "\t 47 & 48 & 696 & 0.08573826 & 0.2169110 & 0.06213175 & 0.2492624 & 0.9836021\\\\\n",
       "\t 48 & 49 & 707 & 0.08540268 & 0.2161872 & 0.06188125 & 0.2487594 & 0.9836874\\\\\n",
       "\t 49 & 50 & 721 & 0.08473154 & 0.2151943 & 0.06158102 & 0.2481552 & 0.9847375\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$OutputCasTables\n",
       ":   \n",
       "A casDataFrame: 1 × 4\n",
       "\n",
       "| casLib &lt;chr&gt; | Name &lt;chr&gt; | Rows &lt;dbl&gt; | Columns &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| CASUSER(sasdemo05) | gbt_scored | 5960 | 8 |\n",
       "\n",
       "\n",
       "$ScoreInfo\n",
       ":   \n",
       "A casDataFrame: 3 × 2\n",
       "\n",
       "| Descr &lt;chr&gt; | Value &lt;chr&gt; |\n",
       "|---|---|\n",
       "| Number of Observations Read |                             5960 |\n",
       "| Number of Observations Used |                             5960 |\n",
       "| Misclassification Error (%) |                     8.4731543624 |\n",
       "\n",
       "\n",
       "$ErrorMetricInfo\n",
       ":   \n",
       "A casDataFrame: 50 × 8\n",
       "\n",
       "| TreeID &lt;dbl&gt; | Trees &lt;dbl&gt; | NLeaves &lt;dbl&gt; | MCR &lt;dbl&gt; | LogLoss &lt;dbl&gt; | ASE &lt;dbl&gt; | RASE &lt;dbl&gt; | MAXAE &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "|  0 |  1 |  13 | 0.19949664 | 0.4583175 | 0.14542261 | 0.3813432 | 0.8162591 |\n",
       "|  1 |  2 |  28 | 0.19949664 | 0.4289321 | 0.13438190 | 0.3665814 | 0.8296554 |\n",
       "|  2 |  3 |  44 | 0.19949664 | 0.4050840 | 0.12522465 | 0.3538709 | 0.8428032 |\n",
       "|  3 |  4 |  60 | 0.19949664 | 0.3859273 | 0.11772208 | 0.3431065 | 0.8550105 |\n",
       "|  4 |  5 |  74 | 0.19949664 | 0.3704777 | 0.11181257 | 0.3343839 | 0.8662366 |\n",
       "|  5 |  6 |  90 | 0.17466443 | 0.3562004 | 0.10643394 | 0.3262421 | 0.8759018 |\n",
       "|  6 |  7 | 106 | 0.14530201 | 0.3442756 | 0.10189294 | 0.3192067 | 0.8837723 |\n",
       "|  7 |  8 | 120 | 0.13322148 | 0.3340708 | 0.09815133 | 0.3132911 | 0.8926982 |\n",
       "|  8 |  9 | 135 | 0.11879195 | 0.3250084 | 0.09485241 | 0.3079812 | 0.9000767 |\n",
       "|  9 | 10 | 150 | 0.11325503 | 0.3166343 | 0.09196214 | 0.3032526 | 0.9064096 |\n",
       "| 10 | 11 | 165 | 0.10989933 | 0.3102472 | 0.08982883 | 0.2997146 | 0.9124400 |\n",
       "| 11 | 12 | 181 | 0.10704698 | 0.3033679 | 0.08753238 | 0.2958587 | 0.9194278 |\n",
       "| 12 | 13 | 195 | 0.10604027 | 0.2976786 | 0.08576177 | 0.2928511 | 0.9261137 |\n",
       "| 13 | 14 | 211 | 0.10520134 | 0.2931048 | 0.08436454 | 0.2904557 | 0.9314005 |\n",
       "| 14 | 15 | 225 | 0.10436242 | 0.2883862 | 0.08292363 | 0.2879646 | 0.9353805 |\n",
       "| 15 | 16 | 241 | 0.10218121 | 0.2844676 | 0.08184719 | 0.2860895 | 0.9406621 |\n",
       "| 16 | 17 | 255 | 0.10285235 | 0.2802390 | 0.08068540 | 0.2840518 | 0.9449353 |\n",
       "| 17 | 18 | 271 | 0.10251678 | 0.2768595 | 0.07969209 | 0.2822979 | 0.9489485 |\n",
       "| 18 | 19 | 287 | 0.10134228 | 0.2736584 | 0.07881852 | 0.2807464 | 0.9531432 |\n",
       "| 19 | 20 | 301 | 0.10100671 | 0.2708183 | 0.07803602 | 0.2793493 | 0.9569792 |\n",
       "| 20 | 21 | 313 | 0.10134228 | 0.2678495 | 0.07706816 | 0.2776115 | 0.9583204 |\n",
       "| 21 | 22 | 327 | 0.10016779 | 0.2650589 | 0.07624769 | 0.2761298 | 0.9614470 |\n",
       "| 22 | 23 | 342 | 0.10000000 | 0.2622113 | 0.07547845 | 0.2747334 | 0.9632765 |\n",
       "| 23 | 24 | 358 | 0.09865772 | 0.2589126 | 0.07445794 | 0.2728698 | 0.9643662 |\n",
       "| 24 | 25 | 372 | 0.09899329 | 0.2565501 | 0.07374377 | 0.2715580 | 0.9657733 |\n",
       "| 25 | 26 | 385 | 0.09781879 | 0.2544412 | 0.07315839 | 0.2704781 | 0.9670358 |\n",
       "| 26 | 27 | 398 | 0.09748322 | 0.2525427 | 0.07267208 | 0.2695776 | 0.9686246 |\n",
       "| 27 | 28 | 412 | 0.09714765 | 0.2503599 | 0.07200030 | 0.2683287 | 0.9691852 |\n",
       "| 28 | 29 | 426 | 0.09714765 | 0.2481523 | 0.07135334 | 0.2671205 | 0.9699781 |\n",
       "| 29 | 30 | 440 | 0.09580537 | 0.2458725 | 0.07065265 | 0.2658057 | 0.9714686 |\n",
       "| 30 | 31 | 456 | 0.09446309 | 0.2440846 | 0.07018587 | 0.2649262 | 0.9727930 |\n",
       "| 31 | 32 | 470 | 0.09412752 | 0.2422631 | 0.06960415 | 0.2638260 | 0.9735549 |\n",
       "| 32 | 33 | 483 | 0.09345638 | 0.2403532 | 0.06903100 | 0.2627375 | 0.9742737 |\n",
       "| 33 | 34 | 494 | 0.09328859 | 0.2387300 | 0.06855810 | 0.2618360 | 0.9745890 |\n",
       "| 34 | 35 | 508 | 0.09295302 | 0.2372782 | 0.06811972 | 0.2609976 | 0.9756157 |\n",
       "| 35 | 36 | 523 | 0.09211409 | 0.2352888 | 0.06756168 | 0.2599263 | 0.9741923 |\n",
       "| 36 | 37 | 538 | 0.09060403 | 0.2328913 | 0.06689158 | 0.2586341 | 0.9759766 |\n",
       "| 37 | 38 | 550 | 0.08959732 | 0.2316356 | 0.06652793 | 0.2579301 | 0.9780755 |\n",
       "| 38 | 39 | 563 | 0.08976510 | 0.2303145 | 0.06613225 | 0.2571619 | 0.9791260 |\n",
       "| 39 | 40 | 579 | 0.08926174 | 0.2278412 | 0.06535282 | 0.2556420 | 0.9794762 |\n",
       "| 40 | 41 | 591 | 0.08909396 | 0.2265814 | 0.06492543 | 0.2548047 | 0.9786891 |\n",
       "| 41 | 42 | 603 | 0.08842282 | 0.2256198 | 0.06464658 | 0.2542569 | 0.9789643 |\n",
       "| 42 | 43 | 617 | 0.08875839 | 0.2243108 | 0.06423675 | 0.2534497 | 0.9790753 |\n",
       "| 43 | 44 | 632 | 0.08892617 | 0.2230472 | 0.06386086 | 0.2527071 | 0.9801133 |\n",
       "| 44 | 45 | 648 | 0.08775168 | 0.2213631 | 0.06345153 | 0.2518959 | 0.9817142 |\n",
       "| 45 | 46 | 664 | 0.08708054 | 0.2200668 | 0.06308301 | 0.2511633 | 0.9822180 |\n",
       "| 46 | 47 | 680 | 0.08691275 | 0.2183415 | 0.06257704 | 0.2501540 | 0.9829572 |\n",
       "| 47 | 48 | 696 | 0.08573826 | 0.2169110 | 0.06213175 | 0.2492624 | 0.9836021 |\n",
       "| 48 | 49 | 707 | 0.08540268 | 0.2161872 | 0.06188125 | 0.2487594 | 0.9836874 |\n",
       "| 49 | 50 | 721 | 0.08473154 | 0.2151943 | 0.06158102 | 0.2481552 | 0.9847375 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$OutputCasTables\n",
       "              casLib       Name Rows Columns\n",
       "1 CASUSER(sasdemo05) gbt_scored 5960       8\n",
       "\n",
       "$ScoreInfo\n",
       "                        Descr                            Value\n",
       "1 Number of Observations Read                             5960\n",
       "2 Number of Observations Used                             5960\n",
       "3 Misclassification Error (%)                     8.4731543624\n",
       "\n",
       "$ErrorMetricInfo\n",
       "   TreeID Trees NLeaves        MCR   LogLoss        ASE      RASE     MAXAE\n",
       "1       0     1      13 0.19949664 0.4583175 0.14542261 0.3813432 0.8162591\n",
       "2       1     2      28 0.19949664 0.4289321 0.13438190 0.3665814 0.8296554\n",
       "3       2     3      44 0.19949664 0.4050840 0.12522465 0.3538709 0.8428032\n",
       "4       3     4      60 0.19949664 0.3859273 0.11772208 0.3431065 0.8550105\n",
       "5       4     5      74 0.19949664 0.3704777 0.11181257 0.3343839 0.8662366\n",
       "6       5     6      90 0.17466443 0.3562004 0.10643394 0.3262421 0.8759018\n",
       "7       6     7     106 0.14530201 0.3442756 0.10189294 0.3192067 0.8837723\n",
       "8       7     8     120 0.13322148 0.3340708 0.09815133 0.3132911 0.8926982\n",
       "9       8     9     135 0.11879195 0.3250084 0.09485241 0.3079812 0.9000767\n",
       "10      9    10     150 0.11325503 0.3166343 0.09196214 0.3032526 0.9064096\n",
       "11     10    11     165 0.10989933 0.3102472 0.08982883 0.2997146 0.9124400\n",
       "12     11    12     181 0.10704698 0.3033679 0.08753238 0.2958587 0.9194278\n",
       "13     12    13     195 0.10604027 0.2976786 0.08576177 0.2928511 0.9261137\n",
       "14     13    14     211 0.10520134 0.2931048 0.08436454 0.2904557 0.9314005\n",
       "15     14    15     225 0.10436242 0.2883862 0.08292363 0.2879646 0.9353805\n",
       "16     15    16     241 0.10218121 0.2844676 0.08184719 0.2860895 0.9406621\n",
       "17     16    17     255 0.10285235 0.2802390 0.08068540 0.2840518 0.9449353\n",
       "18     17    18     271 0.10251678 0.2768595 0.07969209 0.2822979 0.9489485\n",
       "19     18    19     287 0.10134228 0.2736584 0.07881852 0.2807464 0.9531432\n",
       "20     19    20     301 0.10100671 0.2708183 0.07803602 0.2793493 0.9569792\n",
       "21     20    21     313 0.10134228 0.2678495 0.07706816 0.2776115 0.9583204\n",
       "22     21    22     327 0.10016779 0.2650589 0.07624769 0.2761298 0.9614470\n",
       "23     22    23     342 0.10000000 0.2622113 0.07547845 0.2747334 0.9632765\n",
       "24     23    24     358 0.09865772 0.2589126 0.07445794 0.2728698 0.9643662\n",
       "25     24    25     372 0.09899329 0.2565501 0.07374377 0.2715580 0.9657733\n",
       "26     25    26     385 0.09781879 0.2544412 0.07315839 0.2704781 0.9670358\n",
       "27     26    27     398 0.09748322 0.2525427 0.07267208 0.2695776 0.9686246\n",
       "28     27    28     412 0.09714765 0.2503599 0.07200030 0.2683287 0.9691852\n",
       "29     28    29     426 0.09714765 0.2481523 0.07135334 0.2671205 0.9699781\n",
       "30     29    30     440 0.09580537 0.2458725 0.07065265 0.2658057 0.9714686\n",
       "31     30    31     456 0.09446309 0.2440846 0.07018587 0.2649262 0.9727930\n",
       "32     31    32     470 0.09412752 0.2422631 0.06960415 0.2638260 0.9735549\n",
       "33     32    33     483 0.09345638 0.2403532 0.06903100 0.2627375 0.9742737\n",
       "34     33    34     494 0.09328859 0.2387300 0.06855810 0.2618360 0.9745890\n",
       "35     34    35     508 0.09295302 0.2372782 0.06811972 0.2609976 0.9756157\n",
       "36     35    36     523 0.09211409 0.2352888 0.06756168 0.2599263 0.9741923\n",
       "37     36    37     538 0.09060403 0.2328913 0.06689158 0.2586341 0.9759766\n",
       "38     37    38     550 0.08959732 0.2316356 0.06652793 0.2579301 0.9780755\n",
       "39     38    39     563 0.08976510 0.2303145 0.06613225 0.2571619 0.9791260\n",
       "40     39    40     579 0.08926174 0.2278412 0.06535282 0.2556420 0.9794762\n",
       "41     40    41     591 0.08909396 0.2265814 0.06492543 0.2548047 0.9786891\n",
       "42     41    42     603 0.08842282 0.2256198 0.06464658 0.2542569 0.9789643\n",
       "43     42    43     617 0.08875839 0.2243108 0.06423675 0.2534497 0.9790753\n",
       "44     43    44     632 0.08892617 0.2230472 0.06386086 0.2527071 0.9801133\n",
       "45     44    45     648 0.08775168 0.2213631 0.06345153 0.2518959 0.9817142\n",
       "46     45    46     664 0.08708054 0.2200668 0.06308301 0.2511633 0.9822180\n",
       "47     46    47     680 0.08691275 0.2183415 0.06257704 0.2501540 0.9829572\n",
       "48     47    48     696 0.08573826 0.2169110 0.06213175 0.2492624 0.9836021\n",
       "49     48    49     707 0.08540268 0.2161872 0.06188125 0.2487594 0.9836874\n",
       "50     49    50     721 0.08473154 0.2151943 0.06158102 0.2481552 0.9847375\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Score Test Data\n",
    "cas.decisionTree.gbtreeScore(conn,\n",
    "    table=list(name = indata),\n",
    "    modelTable='gbt_model',\n",
    "    copyVars     = list(target, '_PartInd_'),      \n",
    "    assessonerow = TRUE,\n",
    "    casOut       = list(name = 'gbt_scored', replace = T)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assess Performance \n",
    "gbt_assessed <- cas.percentile.assess(conn,\n",
    "                table = list(name = 'gbt_scored', where = '_PartInd_ = 1'),\n",
    "                inputs = '_gbt_P_           1',\n",
    "                response = target,\n",
    "                event = '1')\n",
    "gbt_roc <- gbt_assessed$ROCInfo\n",
    "gbt_roc$Model <- \"CAS Gradient Boosting\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assess Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have built our seven models, now let’s us see how they compare to each other. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "roc.df <- data.frame()\n",
    "# Add R Models \n",
    "roc.df <- rbind(roc.df, rlog_roc, rtree_roc, rboost_roc)\n",
    "# Add CAS Models \n",
    "roc.df <- rbind(roc.df, log_roc, dt_roc, tune_dt_roc, gbt_roc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Confusion Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 7 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Model</th><th scope=col>TP</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>TN</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>R Logistic Regression      </td><td>112</td><td> 51</td><td>236</td><td>1389</td></tr>\n",
       "\t<tr><td>R Decision Tree            </td><td>223</td><td> 92</td><td>125</td><td>1348</td></tr>\n",
       "\t<tr><td>R Gradient Boosting        </td><td>231</td><td> 40</td><td>117</td><td>1400</td></tr>\n",
       "\t<tr><td>CAS Logistic Regression    </td><td>109</td><td> 49</td><td>239</td><td>1391</td></tr>\n",
       "\t<tr><td>CAS Decision Tree          </td><td>264</td><td>153</td><td> 84</td><td>1287</td></tr>\n",
       "\t<tr><td>CAS Autotuned Decision Tree</td><td>227</td><td> 90</td><td>121</td><td>1350</td></tr>\n",
       "\t<tr><td>CAS Gradient Boosting      </td><td>221</td><td> 47</td><td>127</td><td>1393</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 7 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       " Model & TP & FP & FN & TN\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t R Logistic Regression       & 112 &  51 & 236 & 1389\\\\\n",
       "\t R Decision Tree             & 223 &  92 & 125 & 1348\\\\\n",
       "\t R Gradient Boosting         & 231 &  40 & 117 & 1400\\\\\n",
       "\t CAS Logistic Regression     & 109 &  49 & 239 & 1391\\\\\n",
       "\t CAS Decision Tree           & 264 & 153 &  84 & 1287\\\\\n",
       "\t CAS Autotuned Decision Tree & 227 &  90 & 121 & 1350\\\\\n",
       "\t CAS Gradient Boosting       & 221 &  47 & 127 & 1393\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 7 × 5\n",
       "\n",
       "| Model &lt;chr&gt; | TP &lt;dbl&gt; | FP &lt;dbl&gt; | FN &lt;dbl&gt; | TN &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| R Logistic Regression       | 112 |  51 | 236 | 1389 |\n",
       "| R Decision Tree             | 223 |  92 | 125 | 1348 |\n",
       "| R Gradient Boosting         | 231 |  40 | 117 | 1400 |\n",
       "| CAS Logistic Regression     | 109 |  49 | 239 | 1391 |\n",
       "| CAS Decision Tree           | 264 | 153 |  84 | 1287 |\n",
       "| CAS Autotuned Decision Tree | 227 |  90 | 121 | 1350 |\n",
       "| CAS Gradient Boosting       | 221 |  47 | 127 | 1393 |\n",
       "\n"
      ],
      "text/plain": [
       "  Model                       TP  FP  FN  TN  \n",
       "1 R Logistic Regression       112  51 236 1389\n",
       "2 R Decision Tree             223  92 125 1348\n",
       "3 R Gradient Boosting         231  40 117 1400\n",
       "4 CAS Logistic Regression     109  49 239 1391\n",
       "5 CAS Decision Tree           264 153  84 1287\n",
       "6 CAS Autotuned Decision Tree 227  90 121 1350\n",
       "7 CAS Gradient Boosting       221  47 127 1393"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Manipulate the dataframe\n",
    "compare <- subset(roc.df, round(roc.df$CutOff, 2) == 0.5)\n",
    "rownames(compare) <- NULL\n",
    "cf <- compare[,c('Model','TP','FP','FN','TN')]\n",
    "cf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Misclassification**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 7 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Model</th><th scope=col>Misclassification</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>R Gradient Boosting        </td><td>0.08780761</td></tr>\n",
       "\t<tr><td>CAS Gradient Boosting      </td><td>0.09731544</td></tr>\n",
       "\t<tr><td>CAS Autotuned Decision Tree</td><td>0.11800895</td></tr>\n",
       "\t<tr><td>R Decision Tree            </td><td>0.12136465</td></tr>\n",
       "\t<tr><td>CAS Decision Tree          </td><td>0.13255034</td></tr>\n",
       "\t<tr><td>R Logistic Regression      </td><td>0.16051454</td></tr>\n",
       "\t<tr><td>CAS Logistic Regression    </td><td>0.16107383</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 7 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " Model & Misclassification\\\\\n",
       " <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t R Gradient Boosting         & 0.08780761\\\\\n",
       "\t CAS Gradient Boosting       & 0.09731544\\\\\n",
       "\t CAS Autotuned Decision Tree & 0.11800895\\\\\n",
       "\t R Decision Tree             & 0.12136465\\\\\n",
       "\t CAS Decision Tree           & 0.13255034\\\\\n",
       "\t R Logistic Regression       & 0.16051454\\\\\n",
       "\t CAS Logistic Regression     & 0.16107383\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 7 × 2\n",
       "\n",
       "| Model &lt;chr&gt; | Misclassification &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| R Gradient Boosting         | 0.08780761 |\n",
       "| CAS Gradient Boosting       | 0.09731544 |\n",
       "| CAS Autotuned Decision Tree | 0.11800895 |\n",
       "| R Decision Tree             | 0.12136465 |\n",
       "| CAS Decision Tree           | 0.13255034 |\n",
       "| R Logistic Regression       | 0.16051454 |\n",
       "| CAS Logistic Regression     | 0.16107383 |\n",
       "\n"
      ],
      "text/plain": [
       "  Model                       Misclassification\n",
       "1 R Gradient Boosting         0.08780761       \n",
       "2 CAS Gradient Boosting       0.09731544       \n",
       "3 CAS Autotuned Decision Tree 0.11800895       \n",
       "4 R Decision Tree             0.12136465       \n",
       "5 CAS Decision Tree           0.13255034       \n",
       "6 R Logistic Regression       0.16051454       \n",
       "7 CAS Logistic Regression     0.16107383       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a dataframe to compare the misclassification rates\n",
    "compare$Misclassification <- 1 - compare$ACC\n",
    "miss <- compare[order(compare$Misclassification), c('Model','Misclassification')]\n",
    "rownames(miss) <- NULL\n",
    "miss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the improvement in our autotuned decision tree above. It beats out both our R decision tree and our decision default decision tree. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ROC**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8uTJvwQTS0a6v5doO1BXR4ijOAqv95b+znBUZOG7iKEUGbDYHeeiga4o/stQ10i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z7QUi8B8BV10CK2JrqlNlY+xXe6XfCkPS5cX4HdGq4r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\nIxfp8BEaGpqQkJCQkJCenr5mzZrHH3/cE5XcZ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ikpKTt37gRw7Nixw4cP7969OzIyEsDy5cvXrl370UcfDRgw4Ntvv92xY0dsbCyAbdu2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\neATBDgAAwCMIdgAAAB7x/wE9jBRSLCT4vQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add a new column to be used as the ROC curve label\n",
    "roc.df$Models <- paste(roc.df$Model, round(roc.df$C, 3), sep = ' - ')\n",
    "\n",
    "# Create the ROC curve\n",
    "ggplot(data = roc.df[c('FPR', 'Sensitivity', 'Models')],\n",
    "    aes(x = as.numeric(FPR), y = as.numeric(Sensitivity), colour = Models)) +\n",
    "    geom_line() +\n",
    "    labs(x = 'False Positive Rate', y = 'True Positive Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End the session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate}\n",
       "\\end{enumerate}\n"
      ],
      "text/markdown": [
       "\n",
       "\n"
      ],
      "text/plain": [
       "list()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# End the session\n",
    "cas.session.endSession(conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have gone through an example of using SAS Viya with R for analysis. We connected to our CAS server, imported, explored, and cleaned our data, we built three models in R, three in CAS, and one autotuned model in CAS, and we ended by examining our model’s misclassification rates and ROC curves.  And ultimately, we learned how easy it is to leverage SAS and R using SWAT, allowing programmers to utilize the power of SAS Analytics from the language they are comfortable in. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}