Predicting churn with the SPSS random tree algorithm.ipynb

{"nbformat_minor": 1, "cells": [{"source": "", "cell_type": "code", "metadata": {}, "outputs": [], "execution_count": null}, {"source": "# Predicting churn with the SPSS random tree algorithm\n\nThis shows you how to create a predictive model of churn rate by using IBM SPSS Algorithm on Apache Spark version 2.0. You'll learn how to create an SPSS random tree model by using the IBM SPSS Machine Learning API, and how to view the model with IBM SPSS Model Viewer.\n\nBecause it consists of multiple classification and regression trees (CART), you can use random tree algorithms to generate accurate predictive models and solve complex classification and regression problems. Each tree develops from a bootstrap sample that is produced by resampling the original data points with replacement data. During the resampling phase, the best split variable is selected for each node from a specified smaller number of variables that are drawn randomly from the full set of variables. Each tree grows without pruning and then, during the scoring phase, the random tree algorithm aggregates tree scores by majority voting (for classification) or average (for regression).\n\nIn this notebook, you'll create a model with telecommunications data to predict when its customers will leave for a competitor, so that you can take some action to retain the customer.\n    \nTo get the most out of this notebook, you should have some familiarity with the Scala programming language.\n\nThis notebooks runs on Scala 2.11 with Spark 2.0. Some familiarity with Scala is recommended.\n\n## Contents \nThis notebook contains the following main sections:\n\n1. [Load the Telco Churn data to the cloud data repository.](#overview)\n1. [Prepare the data.](#prepare)\n1. [Configure the RandomTrees model.](#configure) \n1. [View the model.](#view)\n1. [Summary and next steps.](#next)    ", "cell_type": "markdown", "metadata": {"collapsed": true}}, {"source": "<a id=\"overview\"></a>\n## 1. Load the Telco Churn data to the cloud data repository\nTelco Churn is a hypothetical data file that concerns a telecommunications company's efforts to reduce turnover in its customer base. Each case corresponds to a separate customer and it records various demographic and service usage information. Before you can work with the data, you must use the URL to get the telco.csv and telco_Feb.csv files from the GitHub repository.\n", "cell_type": "markdown", "metadata": {}}, {"source": "val link_telco = \"https://raw.githubusercontent.com/alokkumar70/TelcoChurn/master/Telco.csv\"\n\nimport sys.process._\nimport java.net.URL\nimport java.io.File\nnew URL(link_telco) #> new File(\"telco.csv\") !!\n\nval link_telco = \"https://raw.githubusercontent.com/alokkumar70/TelcoChurn/master/Telco_Feb.csv\"\n\nimport sys.process._\nimport java.net.URL\nimport java.io.File\nnew URL(link_telco) #> new File(\"telco_Feb.csv\") !!", "cell_type": "code", "metadata": {}, "outputs": [], "execution_count": 15}, {"source": "<a id=\"prepare\"></a>\n## 2. Prepare the data\n\nAfter uploading the CSV files that contain the data, you must create a SQLContext, put the data from the telco.scv file into a Spark DataFrame, and show the first row in the DataFrame.", "cell_type": "markdown", "metadata": {}}, {"source": "val sqlContext = new org.apache.spark.sql.SQLContext(sc)\n\nval dfTelco = sqlContext.\n    read.\n    format(\"com.databricks.spark.csv\").\n    option(\"header\", \"true\").\n    option(\"inferschema\", \"true\").\n    load(\"telco.csv\")\n\ndfTelco.show(3)", "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "+---------------+--------------+-------------+-----------------+------------+-------------+--------------+------------+--------------+-------------+---------+\n|VOICE_OUT_CALLS|VOICE_OUT_MINS|SMS_OUT_CALLS|OUT_CALLS_ROAMING|GPRS_TRAFFIC|MMS_OUT_CALLS|INTERNET_CALLS|TENURE_START|TENURE_RENEWAL|OUT_COMMUNITY|     ARPU|\n+---------------+--------------+-------------+-----------------+------------+-------------+--------------+------------+--------------+-------------+---------+\n|     113.719453|   9731.231668|    53.317815|              0.0|         0.0|     2.651429|           0.0|    8.213843|      8.223275|    18.911935|42.977816|\n|      85.737091|   5733.881441|    20.173326|         3.729763|         0.0|          0.0|           0.0|   10.939408|     11.208829|    28.696075|30.486712|\n|      80.909786|   5299.844355|     3.753237|              0.0|         0.0|          0.0|           0.0|    7.394325|       7.32487|    18.680244|21.567519|\n+---------------+--------------+-------------+-----------------+------------+-------------+--------------+------------+--------------+-------------+---------+\nonly showing top 3 rows\n\n"}], "execution_count": 16}, {"source": "Review the data. Print the schema of the DataFrame to look at what kind of data you have.", "cell_type": "markdown", "metadata": {"collapsed": true}}, {"source": "dfTelco.printSchema", "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "root\n |-- VOICE_OUT_CALLS: double (nullable = true)\n |-- VOICE_OUT_MINS: double (nullable = true)\n |-- SMS_OUT_CALLS: double (nullable = true)\n |-- OUT_CALLS_ROAMING: double (nullable = true)\n |-- GPRS_TRAFFIC: double (nullable = true)\n |-- MMS_OUT_CALLS: double (nullable = true)\n |-- INTERNET_CALLS: double (nullable = true)\n |-- TENURE_START: double (nullable = true)\n |-- TENURE_RENEWAL: double (nullable = true)\n |-- OUT_COMMUNITY: double (nullable = true)\n |-- ARPU: double (nullable = true)\n\n"}], "execution_count": 17}, {"source": "Create a DataFrame for the telco_Feb.csv data. You'll use this year's data to build the model, and use the February data for accuracy value.", "cell_type": "markdown", "metadata": {"collapsed": true}}, {"source": "val dfTelcoFeb = sqlContext.\n    read.\n    format(\"com.databricks.spark.csv\").\n    option(\"header\", \"true\").\n    option(\"inferschema\", \"true\").\n    load(\"telco_Feb.csv\")\ndfTelcoFeb.show(3)", "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "+------+------+---+-------+-------+------+---+------+------+------+------+--------+-----+--------+--------+-------+-------+--------+-------+-------+-------+-------+--------+-------+-------+--------+-----+-----+--------+------+--------+-------+------+-----+----------------+----------------+-------+----------------+----------------+----------------+-------+-----+\n|region|tenure|age|marital|address|income| ed|employ|retire|gender|reside|tollfree|equip|callcard|wireless|longmon|tollmon|equipmon|cardmon|wiremon|longten|tollten|equipten|cardten|wireten|multline|voice|pager|internet|callid|callwait|forward|confer|ebill|         loglong|         logtoll|logequi|         logcard|         logwire|           lninc|custcat|churn|\n+------+------+---+-------+-------+------+---+------+------+------+------+--------+-----+--------+--------+-------+-------+--------+-------+-------+-------+-------+--------+-------+-------+--------+-----+-----+--------+------+--------+-------+------+-----+----------------+----------------+-------+----------------+----------------+----------------+-------+-----+\n|     3|    11| 33|      1|      7|   136|  5|     5|     0|     0|     6|       1|    0|       1|       1|    4.4|  20.75|     0.0|  15.25|   35.7|   42.0| 211.45|     0.0|  125.0| 380.35|       0|    1|    1|       0|     1|       1|      1|     1|    0|1.48160454092422|3.03254624667671|       |2.72457950305342|3.57515068878559|4.91265488573605|      4|    1|\n|     2|    33| 33|      0|     12|    33|  2|     0|     0|     1|     1|       0|    0|       0|       0|   9.45|    0.0|     0.0|    0.0|    0.0|  288.8|    0.0|     0.0|    0.0|    0.0|       0|    0|    0|       0|     0|       0|      0|     0|    0|2.24601474150565|                |       |                |                |3.49650756146648|      1|    1|\n|     2|    41| 39|      0|     17|    78|  2|    16|     0|     1|     1|       1|    0|       1|       0|   11.8|  19.25|     0.0|   13.5|    0.0|  487.4|  798.4|     0.0|  570.0|    0.0|       0|    0|    0|       0|     1|       1|      0|     0|    0|2.46809953147162|2.95751106073379|       |2.60268968544438|                |4.35670882668959|      3|    0|\n+------+------+---+-------+-------+------+---+------+------+------+------+--------+-----+--------+--------+-------+-------+--------+-------+-------+-------+-------+--------+-------+-------+--------+-----+-----+--------+------+--------+-------+------+-----+----------------+----------------+-------+----------------+----------------+----------------+-------+-----+\nonly showing top 3 rows\n\n"}], "execution_count": 24}, {"source": "<a id=\"configure\"></a>\n## 3. Configure the RandomTrees model\n\nBy running this portion of the code, you create the random trees estimator, import the libraries, and set the ordinal and nominal variables. Because no inputFieldList value is set, all fields except the target, frequency, and analysis weight fields are treated as input fields. To make the random tree model build faster, set the number of trees to 10 instead of the default value, which is 100. Finally, you must specify the churn target field. ", "cell_type": "markdown", "metadata": {}}, {"source": "import com.ibm.spss.ml.classificationandregression.ensemble.RandomTrees\nimport com.ibm.spss.ml.utils.DataFrameImplicits.DataFrameEnrichImplicitsClass\nval ordinal = Array(\"ed\")\nval nominal = Array(\"region\",\"marital\",\"retire\",\"gender\", \"tollfree\",\"equip\",\"callcard\",\"wireless\",\"multline\",\"voice\",\"pager\",\"internet\",\"callid\",\"callwait\",\"forward\",\"confer\",\"ebill\",\"custcat\",\"CHURN\")\n\nval srf = RandomTrees().setTargetField(\"churn\").setNumTrees(10)\nval srfModel = srf.fit(dfTelcoFeb.setNominalMeasure(nominal,true).setOrdinalMeasure(ordinal,true))", "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "                                                                                \r"}], "execution_count": 32}, {"source": "Do the prediction and get your results.", "cell_type": "markdown", "metadata": {}}, {"source": "val predResult = srfModel.transform(dfTelcoFeb)\nval predResultNew = predResult.withColumn(\"prediction\", predResult(\"prediction\").cast(\"double\")).\n    withColumn(\"churn\", predResult(\"churn\").cast(\"double\"))", "cell_type": "code", "metadata": {}, "outputs": [], "execution_count": 33}, {"source": "To get the accuracy result, use the Apache Spark **MulticlassClassificationEvaluator** function. Notice that the accuracy is above 90%.", "cell_type": "markdown", "metadata": {}}, {"source": "import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator\nval evaluator = new MulticlassClassificationEvaluator().setLabelCol(\"churn\").setMetricName(\"accuracy\")\nval acc_result = evaluator.evaluate(predResultNew)\nprintln(s\"acc_result:$acc_result\")", "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "acc_result:0.942\n"}], "execution_count": 8}, {"source": "<a id=\"view\"></a>\n## 4. View the model\n\nView the model with the SPSS Model Viewer. The visualization for the generalized linear model includes a confusion matrix, a table with top decision rules, and a table and chart of predictor importance.\n\n### 4.1 Generate a project token\n\nBefore you can run the model viewer, you need to generate a project token\n\n1. In the **My Projects** banner, click the **More** icon and then click **Insert project token**. The project token is inserted into the first cell of the notebook, before the title.\n2. Copy the text, which appears at the beginning of the notebook, into the following cell and run it.", "cell_type": "markdown", "metadata": {}}, {"source": "// @hidden_cell\n// The project token is an authorization token that is used to access project resources like data sources, connections, and used by platform APIs.\nimport com.ibm.analytics.projectNotebookIntegration._\nval pc = ProjectUtil.newProjectContext(sc, \"0863b9f0-8ed3-4fc6-bf1d-5d5bd8f74671\", \"p-dbbeaf8e0c0f328ee67a791d85409b533b081153\")\n", "cell_type": "code", "metadata": {}, "outputs": [], "execution_count": 10}, {"source": "### 4.2 Start the model viewer\n\nRun the code in the following cell to start SPSS Model Viewer, where you can see a visualization and see model statistics and other characteristics.", "cell_type": "markdown", "metadata": {}}, {"source": "import com.ibm.spss.scala.ModelViewer\nkernel.magics.html(ModelViewer.toHTML(pc,srfModel))", "cell_type": "code", "metadata": {}, "outputs": [{"output_type": 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data-model-viewer-uuid=\"_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" data-model=\"spss.randomTrees\" data-caller=\"dsx-notebook\">\n\n\n  <div id=\"top_most_div_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" class=\"mdlvr\">\n    <div class='MVInfotooltip'>\n      <a id=\"showMenuButton_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" class=\"show_menu_svg\"\n          onclick=\"showHideLeftPane('left_pane_div_6ab88938_2dfe_47a0_a8b4_9b752e28731b', 'right_pane_div_6ab88938_2dfe_47a0_a8b4_9b752e28731b', 'hideMenuButton_6ab88938_2dfe_47a0_a8b4_9b752e28731b', 'showMenuButton_6ab88938_2dfe_47a0_a8b4_9b752e28731b', true)\"\n          style=\"background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/1.0.0/images/carbon/menu.svg); display: none;\">\n      </a>\n      <span style=\"width: 100px; transform: translate(36px, -16px);\" class=\"MVInfotooltiptext\">Open Navigation</span>\n    </div>\n    <div class='MVInfotooltip'>\n      <a id=\"hideMenuButton_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" class=\"hide_menu_svg\"\n          onclick=\"showHideLeftPane('left_pane_div_6ab88938_2dfe_47a0_a8b4_9b752e28731b', 'right_pane_div_6ab88938_2dfe_47a0_a8b4_9b752e28731b', 'hideMenuButton_6ab88938_2dfe_47a0_a8b4_9b752e28731b', 'showMenuButton_6ab88938_2dfe_47a0_a8b4_9b752e28731b', false)\"\n          style=\"background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/1.0.0/images/carbon/close.svg);\">\n      </a>\n      <span style=\"width: 100px; transform: translate(285px, -16px);\" class=\"MVInfotooltiptext\">Close Navigation</span>\n    </div>\n    <div id=\"left_pane_div_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" class=\"mdlvr left_pane_div\">\n      <div class=\"leftTitle\">Random Trees\n        <div class='MVInfotooltip'>\n          <a class='infodot' style='background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/1.0.0/images/carbon/info_small_18.svg);'></a>\n            <span class='MVInfotooltiptext'>The Random Trees algorithm is a sophisticated modern approach to supervised learning for categorical or continuous targets. The algorithm uses groups of classification or regression trees and randomness to make predictions that are particularly robust when applied to new observations.</span>\n        </div>\n      </div>\n      <hr class=\"leftSectionSplitter\">\n      <div class=\"mdlvr left_pane_menu_div\" style=\"overflow-y:auto;\">\n        <ul class=\"menuList\">\n        <p class=\"menuList\">EVALUATION</p>\n          <li class=\"menuList\"><a class=\"showModelVisualizationMenuItem_6ab88938_2dfe_47a0_a8b4_9b752e28731b active\" id=\"showModelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_1\" data-mv-type=\"chart\">Model Evaluation</a></li>\n          <li class=\"menuList\"><a class=\"showModelVisualizationMenuItem_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" id=\"showModelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_2\" data-mv-type=\"table\">Confusion Matrix</a></li>\n        <p class=\"menuList\">MODEL VIEWER</p>\n          <li class=\"menuList\"><a class=\"showModelVisualizationMenuItem_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" id=\"showModelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_3\" data-mv-type=\"table\">Model Information</a></li>\n          <li class=\"menuList\"><a class=\"showModelVisualizationMenuItem_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" id=\"showModelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_4\" data-mv-type=\"table\">Records Summary</a></li>\n          <li class=\"menuList\"><a class=\"showModelVisualizationMenuItem_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" id=\"showModelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_5\" data-mv-type=\"chart\">Predictor Importance</a></li>\n          <li class=\"menuList\"><a class=\"showModelVisualizationMenuItem_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" id=\"showModelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_6\" data-mv-type=\"table\">Top Decision Rules</a></li>\n        </ul>\n      </div>\n    </div>\n  </div>\n\n    <div id=\"right_pane_div_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" class=\"mdlvr right_pane_div\" style=\"overflow: auto;\">\n      <div class=\"vTitle1\" id=\"vTitle1_6ab88938_2dfe_47a0_a8b4_9b752e28731b\">&nbsp;</div>\n      <div class=\"vTitle2\" id=\"vTitle2_6ab88938_2dfe_47a0_a8b4_9b752e28731b\">&nbsp;</div>\n      <hr class=\"sectionSplitter\">\n      <div class=\"titleBuffer\"></div>\n\n      <div class=\"modelVisualizationDiv modelVisualizationDiv_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" id=\"modelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_1\" style=\"\">\n        <div class=\"chartDiv vDiv combo_top_pane_div\" style=\"height: 20%; width: 90%;\" data-t1=\"Model Evaluation\" data-id=\"spss.randomTrees.chart.ModelAccuracy\" data-t2=\"Target : churn\" data-t3=\"<div class='MVInfotooltip'><a ondblclick='MdlVizChart.printChart(modelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_1)' class='infodot' style='background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/1.0.0/images/carbon/info_small_18.svg);'><span class='MVInfotooltiptext'>For classification models, the Model Evaluation panel shows a bar graph displaying the overall prediction accuracy, or proportion of correct predictions, and a table containing a set of evaluation statistics. For regression models, the panel shows a bar graph displaying the R<sup>2</sup> as a measure of prediction accuracy, and a table with a set of evaluation statistics. (In either case if the prediction accuracy is exactly 0, the graph will not be shown.) Accuracy estimates are based on the final ensemble model predictions, not on out-of-bag instances during model building, as is the case with estimates shown in the Model Information Table. </span><a></div>\">\n          <div class=\"interactionContainer\">\n            <div class=\"interactionSubContainer\" style=\"display: block;\">\n              <div class=\"accuracyLabel\" >Model Accuracy</div>\n                <div style=\"display: flex;\">\n              <div class=\"interactionChart\" id=\"ModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" style=\"width:280px;height:20px\">\n                <style>\n#visualizationModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b.brunel .chart1 .element.bar {\n\tstroke: transparent;\n\tstroke-width: 1px;\n\twidth: 20px;\n}\n\n#visualizationModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b.brunel .chart1 .element1 .element {\n\twidth: 20px;\n}\n                </style>\n                    <link rel=\"stylesheet\" href=\"https://code.jquery.com/ui/1.12.1/themes/base/jquery-ui.css\">\n                <svg id=\"visualizationModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" data-name=\"ModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" width=\"280\" height=\"20\"></svg>\n                <script>\n                  var vModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b;\n                  var tableModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b;\n    require.config(\n        {\n            waitSeconds: 60,\n            paths: {\n                'd3': '//cdnjs.cloudflare.com/ajax/libs/d3/4.2.1/d3.min',\n                'topojson' : '//cdnjs.cloudflare.com/ajax/libs/topojson/1.6.20/topojson.min',\n                'brunel' : 'https://model-viewer.mybluemix.net/mdlvr/assets/3PP/javascripts/brunel/brunel.2.5.min',\n                'brunelControls' : 'https://model-viewer.mybluemix.net/mdlvr/assets/3PP/javascripts/brunel/brunel.controls.2.5.min'\n            },\n            shim: {\n                'brunel' : {\n                    exports: 'BrunelD3',\n                    deps: ['d3', 'topojson'],\n                    init: function() {return {BrunelD3 : BrunelD3,BrunelData : BrunelData}}\n                },\n                'brunelControls' : {\n                    exports: 'BrunelEventHandlers',\n                    init: function() {return {BrunelEventHandlers: BrunelEventHandlers,BrunelJQueryControlFactory: BrunelJQueryControlFactory}}\n                }\n            }\n        }\n    );\n\n    require(\n        [\"d3\"], \n        function(d3) {\n            require(\n                [\"brunel\", \"brunelControls\"], function(brunel, brunelControls) {\nfunction  BrunelVisModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b(visId) {\n  \"use strict\";                                                                       // strict mode\n  var datasets = [],                                      // array of datasets for the original data\n      pre = function(d, i) { return d },                         // default pre-process does nothing\n      post = function(d, i) { return d },                       // default post-process does nothing\n      transitionTime = 200,                                        // transition time for animations\n      charts = [],                                                       // the charts in the system\n      vis = d3.select('#' + visId).attr('class', 'brunel');                     // the SVG container\n\n  BrunelD3.addDefinitions(vis);                                   // ensure standard symbols present\n\n  // Define chart #1 in the visualization //////////////////////////////////////////////////////////\n\n  charts[0] = function(parentNode, filterRows) {\n    var geom = BrunelD3.geometry(parentNode || vis.node(), 0, 0, 1, 1, 0, 0, 0, 0),\n      elements = [];                                              // array of elements in this chart\n    geom.transpose();\n\n    // Define groups for the chart parts ///////////////////////////////////////////////////////////\n\n    var chart =  vis.append('g').attr('class', 'chart1')\n      .attr('transform','translate(' + geom.chart_left + ',' + geom.chart_top + ')');\n    var overlay = chart.append('g').attr('class', 'element').attr('class', 'overlay');\n    var zoom = d3.zoom().scaleExtent([1/3,3]);\n    var zoomNode = overlay.append('rect').attr('class', 'overlay')\n      .attr('x', geom.inner_left).attr('y', geom.inner_top)\n      .attr('width', geom.inner_rawWidth).attr('height', geom.inner_rawHeight)\n      .style('cursor', 'default')\n      .node();\n    zoomNode.__zoom = d3.zoomIdentity;\n    chart.append('rect').attr('class', 'background').attr('width', geom.chart_right-geom.chart_left).attr('height', geom.chart_bottom-geom.chart_top);\n    var interior = chart.append('g').attr('class', 'interior zoomNone')\n      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')')\n      .attr('clip-path', 'url(#clip_visualization_chart_ModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b_inner)');\n    interior.append('rect').attr('class', 'inner').attr('width', geom.inner_width).attr('height', geom.inner_height);\n    var gridGroup = interior.append('g').attr('class', 'grid');\n    vis.select('defs').append('clipPath').attr('id', 'clip_visualization_chart_ModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b_inner').append('rect')\n      .attr('x', 0).attr('y', 0)\n      .attr('width', geom.inner_rawWidth+1).attr('height', geom.inner_rawHeight+1);\n\n    // Scales //////////////////////////////////////////////////////////////////////////////////////\n\n    var scale_x = d3.scaleLinear().domain([0,1])\n      .range([geom.inner_width, 0]);\n    var scale_inner = d3.scaleLinear().domain([0,1])\n      .range([-0.5, 0.5]);\n    var scale_y = d3.scaleLinear().domain([0, 1.0000001])\n      .range([0, geom.inner_height]);\n    var base_scales = [scale_x, scale_y];                           // untransformed original scales\n    zoom.on('zoom', function(t, time) {\n        t = t ||BrunelD3.restrictZoom(d3.event.transform, geom, this);\n        zoomNode.__zoom = t;\n        interior.attr('class', 'interior ' + BrunelD3.zoomLabel(t.k));;\n        build(time || -1);\n    });\n\n    // Define element #1 ///////////////////////////////////////////////////////////////////////////\n\n    elements[0] = function() {\n      var original, processed,                           // data sets passed in and then transformed\n        element, data,                                 // brunel element information and brunel data\n        selection, merged;                                      // d3 selection and merged selection\n      var elementGroup = interior.append('g').attr('class', 'element1')\n        .attr('role', 'region').attr('aria-label', 'Accuracy as bars, also showing numberLabel')\n        .attr('transform','matrix(0,1,1,0,0,0)'),\n        main = elementGroup.append('g').attr('class', 'main'),\n        labels = BrunelD3.undoTransform(elementGroup.append('g').attr('class', 'labels').attr('aria-hidden', 'true'), elementGroup);\n\n      function makeData() {\n        original = datasets[0];\n        if (filterRows) original = original.retainRows(filterRows);\n        processed = pre(original, 0)\n          .stack('Accuracy; ; numberLabel; false');\n        processed = post(processed, 0);\n        var f0 = processed.field('Accuracy$lower'),\n          f1 = processed.field('Accuracy$upper'),\n          f2 = processed.field('Accuracy'),\n          f3 = processed.field('numberLabel'),\n          f4 = processed.field('#row'),\n          f5 = processed.field('#selection');\n        var keyFunc = function(d) { return f3.value(d) };\n        data = {\n          Accuracy$lower:function(d) { return f0.value(d.row) },\n          Accuracy$upper:function(d) { return f1.value(d.row) },\n          Accuracy:     function(d) { return f2.value(d.row) },\n          numberLabel:  function(d) { return f3.value(d.row) },\n          $row:         function(d) { return f4.value(d.row) },\n          $selection:   function(d) { return f5.value(d.row) },\n          Accuracy$lower_f:function(d) { return f0.valueFormatted(d.row) },\n          Accuracy$upper_f:function(d) { return f1.valueFormatted(d.row) },\n          Accuracy_f:   function(d) { return f2.valueFormatted(d.row) },\n          numberLabel_f:function(d) { return f3.valueFormatted(d.row) },\n          $row_f:       function(d) { return f4.valueFormatted(d.row) },\n          $selection_f: function(d) { return f5.valueFormatted(d.row) },\n          _split:       function(d) { return f3.value(d.row) },\n          _key:         keyFunc,\n          _rows:        BrunelD3.makeRowsWithKeys(keyFunc, processed.rowCount())\n        };\n      }\n      // Aesthetic Functions\n      var scale_color = d3.scaleOrdinal()\n        .domain(['a_notAccurate', 'b_accurate'])\n        .range([ '#E8EBEE', '#42CBDB']);\n      var color = function(d) { var c = data.numberLabel(d); return c!=null ? scale_color(c) : null };\n\n      // Build element from data ///////////////////////////////////////////////////////////////////\n\n      function build(transitionMillis) {\n        element = elements[0];\n        var w = 20.0;\n        var x1 = scale_x.range()[0];\n        var x2 = scale_x.range()[1];\n        var h = Math.abs( scale_y(scale_y.domain()[0] + 0.30500000000000005) - scale_y.range()[0] );\n        var y1 = function(d) { return scale_y(data.Accuracy$lower(d))};\n        var y2 = function(d) { return scale_y(data.Accuracy$upper(d))};\n        var y = function(d) { return scale_y( (data.Accuracy$upper(d) + data.Accuracy$lower(d) )/2)};\n\n        // Define selection entry operations\n        function initialState(selection) {\n          selection\n            .attr('class', 'element bar filled')\n            .style('pointer-events', 'none')\n            .attr('role', 'img').attr('aria-label', \n          function(d) { return data._key(d.row);\n          })\n            .each(function(d) {\n              var a = x1, b = x2, left = Math.min(a,b), width = Math.max(1e-6, Math.abs(a-b)), \n              c = y1(d), d = y2(d), top = Math.min(c,d), height = Math.max(1e-6, Math.abs(c-d));\n              this.r = {x:left, y:top, w:width, h:height};\n            })\n            .attr('x', function(d) { return this.r.x })\n            .attr('y', function(d) { return this.r.y + this.r.h/2 })\n            .attr('width', function(d) { return this.r.w })\n            .attr('height',0)\n        }\n\n        // Define selection update operations on merged data\n        function updateState(selection) {\n          selection\n            .each(function(d) {\n              var a = x1, b = x2, left = Math.min(a,b), width = Math.max(1e-6, Math.abs(a-b)), \n              c = y1(d), d = y2(d), top = Math.min(c,d), height = Math.max(1e-6, Math.abs(c-d));\n              this.r = {x:left, y:top, w:width, h:height};\n            })\n            .attr('x', function(d) { return this.r.x })\n            .attr('y', function(d) { return this.r.y })\n            .attr('width', function(d) { return this.r.w })\n            .attr('height', function(d) { return this.r.h })\n            .filter(BrunelD3.hasData)                     // following only performed for data items\n            .style('fill', color);\n        }\n\n        // Define labeling for the selection\n        function label(selection, transitionMillis) {\n        }\n        // Create selections, set the initial state and transition updates\n        selection = main.selectAll('.element').data(data._rows, function(d) { return d.key });\n        var added = selection.enter().append('rect');\n        merged = selection.merge(added);\n        initialState(added);\n        selection.filter(BrunelD3.hasData)\n          .classed('selected', BrunelD3.isSelected(data))\n          .filter(BrunelD3.isSelected(data)).raise();\n        updateState(BrunelD3.transition(merged, transitionMillis));\n\n        selection.exit().each(function() { this.remove(); BrunelD3.removeLabels(this)} );\n      }\n\n      return {\n        data:           function() { return processed },\n        original:       function() { return original },\n        internal:       function() { return data },\n        selection:      function() { return merged },\n        makeData:       makeData,\n        build:          build,\n        chart:          function() { return charts[0] },\n        group:          function() { return elementGroup },\n        fields: {\n          y:            ['Accuracy'],\n          key:          ['numberLabel'],\n          color:        ['numberLabel']\n        }\n      };\n    }();\n\n    function build(time, noData) {\n      var first = elements[0].data() == null;\n      if (first) time = 0;                                           // no transition for first call\n      if ((first || time > -1) && !noData) {\n        elements[0].makeData();\n      }\n      elements[0].build(time);\n    }\n\n    // Expose the following components of the chart\n    return {\n      elements : elements,\n      interior : interior,\n      scales: {x:scale_x, y:scale_y},\n      zoom: function(params, time) {\n          if (params) zoom.on('zoom').call(zoomNode, params, time);\n          return d3.zoomTransform(zoomNode);\n      },\n      build : build\n    };\n    }();\n\n  function setData(rowData, i) { datasets[i||0] = BrunelD3.makeData(rowData) }\n  function updateAll(time) { charts.forEach(function(x) {x.build(time || 0)}) }\n  function buildAll() {\n    for (var i=0;i<arguments.length;i++) setData(arguments[i], i);\n    updateAll(transitionTime);\n  }\n\n  return {\n    dataPreProcess:     function(f) { if (f) pre = f; return pre },\n    dataPostProcess:    function(f) { if (f) post = f; return post },\n    data:               function(d,i) { if (d) setData(d,i); return datasets[i||0] },\n    visId:              visId,\n    build:              buildAll,\n    rebuild:            updateAll,\n    charts:             charts\n  }\n}\n\n// Data Tables /////////////////////////////////////////////////////////////////////////////////////\n\ntableModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b = {\n   summarized: false,\n   names: ['Accuracy', 'numberLabel'], \n   options: ['numeric', 'string'], \n   rows: [[0.305, 'a_notAccurate'], [0.695, 'b_accurate']]\n};\n\n// Call Code to Build the system ///////////////////////////////////////////////////////////////////\n\nvModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b = new BrunelVisModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b('visualizationModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b');\nBrunelD3.animateBuild(vModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b, tableModelAccuracy_6ab88938_2dfe_47a0_a8b4_9b752e28731b, 700);\n\n\nfunction  hideInteractiveControls() {\n\t$('.interactionContainer').css('data-width-adj', 0);\n\t$('.interactionContainer').css('data-height-adj', 0);\n\t$('.otherChartZoom').hide();\n}\n\n\t               }\n            );\n        }\n    );\n                </script>\n              </div>\n<div class='MAtooltip accuracyLabel' style=\"font-size: 36px; height:30px; text-align: center; margin-top:-15px; margin-left: 35px;\"><span class='MAtooltiptext'>0.6952573158425832</span>0.695</div>\n</div>\n\n            </div>\n          </div>\n\n        </div>\n        <div id=\"tableDiv_6ab88938_2dfe_47a0_a8b4_9b752e28731b_2\" class=\"tableDiv vDiv combo_bottom_pane_div\" style=\"top: 20%;\" data-t1=\"Model Evaluation Measures\" data-id=\"spss.randomTrees.table.AdditionalMeasures\" data-t2=\"Target : churn\" data-t3=\"<div class='MVInfotooltip'><a ondblclick='MdlVizTable.printTable(modelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_2)' class='infodot' style='background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/1.0.0/images/carbon/info_small_18.svg);'><span class='MVInfotooltiptext'>For classification models, the Model Evaluation panel shows a bar graph displaying the overall prediction accuracy, or proportion of correct predictions, and a table containing a set of evaluation statistics. For regression models, the panel shows a bar graph displaying the R<sup>2</sup> as a measure of prediction accuracy, and a table with a set of evaluation statistics. (In either case if the prediction accuracy is exactly 0, the graph will not be shown.) Accuracy estimates are based on the final ensemble model predictions, not on out-of-bag instances during model building, as is the case with estimates shown in the Model Information Table. </span><a></div>\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\" data-id=\"Model_Evaluation_Measures\" id=\"Model_Evaluation_Measures_6ab88938_2dfe_47a0_a8b4_9b752e28731b\"><caption>Model Evaluation Measures</caption><tbody class=\"mdlvr-table-body\"><tr><th tabindex=\"0\">Classification Accuracy</th><td tabindex=\"0\" data-num=\"0.695257315842583\">0.695</td></tr><tr><th tabindex=\"0\">Weighted True Positive Rate</th><td tabindex=\"0\" data-num=\"0.695257315842583\">0.695</td></tr><tr><th tabindex=\"0\">Weighted False Positive Rate</th><td tabindex=\"0\" data-num=\"0.464989582880135\">0.465</td></tr><tr><th tabindex=\"0\">Weighted Precision</th><td tabindex=\"0\" data-num=\"0.69386739902324\">0.694</td></tr><tr><th tabindex=\"0\">Weighted Recall</th><td tabindex=\"0\" data-num=\"0.695257315842583\">0.695</td></tr><tr><th tabindex=\"0\">Weighted F<sub>1</sub> Measure</th><td tabindex=\"0\" data-num=\"0.694551305925519\">0.695</td></tr></tbody></table>\n        </div>\n      </div>\n\n      <div class=\"modelVisualizationDiv modelVisualizationDiv_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" id=\"modelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_2\" style=\"display: none;\">\n        <div id=\"tableDiv_6ab88938_2dfe_47a0_a8b4_9b752e28731b_2\" class=\"tableDiv vDiv hasDataTable\" data-t1=\"Confusion Matrix\" data-id=\"spss.randomTrees.table.ConfusionMatrix\" data-t2=\"Target : churn\" data-t3=\"<div class='MVInfotooltip'><a ondblclick='MdlVizTable.printTable(modelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_2)' class='infodot' style='background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/1.0.0/images/carbon/info_small_18.svg);'><span class='MVInfotooltiptext'>The confusion matrix or classification table contains a cross-classification of observed by predicted labels or groups. The numbers of correct predictions are shown in the cells along the main diagonal. Correct percentages are shown for each row, column and overall.</span><a></div>\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\" data-id=\"Confusion_Matrix\" data-tableSettings=\"'scrollX':'100%','scrollY':'60vh','scrollCollapse':true,'searching':false,'paging':false,'info':false,'bFilter':false,'ordering':false\" id=\"Confusion_Matrix_6ab88938_2dfe_47a0_a8b4_9b752e28731b\"><caption>Confusion Matrix</caption><thead class=\"OriginalHeader\"><tr><th rowspan=\"2\" tabindex=\"0\">Observed</th><th colspan=\"3\" tabindex=\"0\">Predicted</th></tr><tr><th tabindex=\"0\">1</th><th tabindex=\"0\">0</th><th tabindex=\"0\">Percent Correct</th></tr></thead><tbody class=\"mdlvr-table-body\"><tr><th tabindex=\"0\">1</th><td tabindex=\"0\">119</td><td tabindex=\"0\">153</td><td tabindex=\"0\" data-num=\"43.75\">43.8%</td></tr><tr><th tabindex=\"0\">0</th><td tabindex=\"0\">149</td><td tabindex=\"0\">570</td><td tabindex=\"0\" data-num=\"79.27677329624478\">79.3%</td></tr><tr><th tabindex=\"0\">Percent Correct</th><td tabindex=\"0\" data-num=\"44.40298507462687\">44.4%</td><td tabindex=\"0\" data-num=\"78.83817427385893\">78.8%</td><td tabindex=\"0\" data-num=\"69.52573158425832\">69.5%</td></tr></tbody></table>\n        </div>\n      </div>\n\n      <div class=\"modelVisualizationDiv modelVisualizationDiv_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" id=\"modelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_3\" style=\"display: none;\">\n        <div id=\"tableDiv_6ab88938_2dfe_47a0_a8b4_9b752e28731b_3\" class=\"tableDiv vDiv\" data-t1=\"Model Information\" data-id=\"spss.randomTrees.table.ModelInformation\" data-t2=\"Target : churn\" data-t3=\"<div class='MVInfotooltip'><a ondblclick='MdlVizTable.printTable(modelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_3)' class='infodot' style='background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/1.0.0/images/carbon/info_small_18.svg);'><span class='MVInfotooltiptext'>This table contains information on the type of model fitted, identifies the target field, the number of input features and estimates of accuracy for out-of-bag instances.</span><a></div>\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\" data-id=\"Model_Information\" id=\"Model_Information_6ab88938_2dfe_47a0_a8b4_9b752e28731b\"><caption>Model Information</caption><tbody class=\"mdlvr-table-body\"><tr><th tabindex=\"0\">Target Field</th><td tabindex=\"0\" class=\"string\">churn</td></tr><tr><th tabindex=\"0\">Model Building Method</th><td tabindex=\"0\" class=\"string\">Random Trees Classification</td></tr><tr><th tabindex=\"0\">Number of Predictors Input</th><td tabindex=\"0\">36</td></tr><tr><th tabindex=\"0\">Estimated Model Accuracy</th><td tabindex=\"0\" data-num=\"0.695257315842583\">0.695</td></tr><tr><th tabindex=\"0\">Estimated Misclassification Rate</th><td tabindex=\"0\" data-num=\"0.304742684157417\">0.305</td></tr></tbody></table>\n        </div>\n      </div>\n\n      <div class=\"modelVisualizationDiv modelVisualizationDiv_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" id=\"modelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_4\" style=\"display: none;\">\n        <div id=\"tableDiv_6ab88938_2dfe_47a0_a8b4_9b752e28731b_4\" class=\"tableDiv vDiv hasDataTable\" data-t1=\"Records Summary\" data-id=\"spss.randomTrees.table.RecordsSummary\" data-t2=\"Target : churn\" data-t3=\"<div class='MVInfotooltip'><a ondblclick='MdlVizTable.printTable(modelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_4)' class='infodot' style='background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/1.0.0/images/carbon/info_small_18.svg);'><span class='MVInfotooltiptext'>Shows the number and percentage of records included and excluded from the analysis.</span><a></div>\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\" data-id=\"Records_Summary\" data-tableSettings=\"'scrollX':'100%','scrollY':'60vh','scrollCollapse':true,'searching':false,'paging':false,'info':false,'bFilter':false,'ordering':false\" id=\"Records_Summary_6ab88938_2dfe_47a0_a8b4_9b752e28731b\"><caption>Records Summary</caption><thead class=\"OriginalHeader\"><tr><th tabindex=\"0\">Records</th><th tabindex=\"0\">Number</th><th tabindex=\"0\">Percent</th></tr></thead><tbody class=\"mdlvr-table-body\"><tr><th tabindex=\"0\">Included</th><td tabindex=\"0\">1,000</td><td tabindex=\"0\">100.00</td></tr><tr><th tabindex=\"0\">Excluded</th><td tabindex=\"0\">0</td><td tabindex=\"0\">0.00</td></tr><tr><th tabindex=\"0\">Total</th><td tabindex=\"0\">1,000</td><td tabindex=\"0\">100.00</td></tr></tbody></table>\n        </div>\n      </div>\n\n      <div class=\"modelVisualizationDiv modelVisualizationDiv_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" id=\"modelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_5\" style=\"display: none;\">\n        <div class=\"chartDiv vDiv\" data-t1=\"Predictor Importance\" data-id=\"spss.randomTrees.chart.PredictorImportanceJS\" data-t2=\"Target : churn\" data-t3=\"<div class='MVInfotooltip'><a ondblclick='MdlVizChart.printChart(modelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_5)' class='infodot' style='background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/1.0.0/images/carbon/info_small_18.svg);'><span class='MVInfotooltiptext'>This chart displays bars representing the predictors in descending order of relative importance for predicting the target. The values for each predictor are scaled so that they add to 1.</span><a></div>\">\n          <div class=\"interactionContainer\">\n            <div class=\"interactionSubContainer\"  resizable=\"both\" >\n              <div class=\"interactionChart\" id=\"PredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" style=\"width:427px;height:370px\">\n                <style>\n#visualizationPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b.brunel .chart1 .label {\n\tfont-size: 12px;\n\tlabel-location: outside-top-left;\n\tpadding: 1px;\n\ttext-align: end;\n}\n\n#visualizationPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b.brunel .chart1 .element.bar {\n\twidth: 10px;\n}\n\n#visualizationPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b.brunel .chart1 .axis .tick line {\n\tsize: 8px;\n\tstroke-width: 1px;\n}\n\n#visualizationPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b.brunel .chart1 .axis.y .title {\n\tpadding-left: 5px;\n}\n\n#visualizationPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b.brunel .chart1 .axis.x .title {\n\tpadding-bottom: 5px;\n}\n\n#visualizationPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b.brunel .chart1 .element.bar.selected {\n\tfill: #00b6cb;\n}\n\n#visualizationPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b.brunel .chart1 .axis.y .tick text {\n\tfont-size: 12px;\n\tpadding-left: 15px;\n\tpadding-right: 5px;\n}\n\n#visualizationPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b.brunel .chart1 .axis.x .tick text {\n\tfont-size: 12px;\n\tpadding-bottom: 15px;\n\tpadding-top: 5px;\n}\n                </style>\n                <svg id=\"visualizationPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" data-name=\"PredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" width=\"427\" height=\"370\"></svg>\n                <script>\n                  var vPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b;\n                  var tablePredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b;\nvar piChartVarName;\nvar currentSelected;\nvar currentSelectedKey;\nvar selected;\n    require.config(\n        {\n            waitSeconds: 60,\n            paths: {\n                'd3': '//cdnjs.cloudflare.com/ajax/libs/d3/4.2.1/d3.min',\n                'topojson' : '//cdnjs.cloudflare.com/ajax/libs/topojson/1.6.20/topojson.min',\n                'brunel' : 'https://model-viewer.mybluemix.net/mdlvr/assets/3PP/javascripts/brunel/brunel.2.5.min',\n                'brunelControls' : 'https://model-viewer.mybluemix.net/mdlvr/assets/3PP/javascripts/brunel/brunel.controls.2.5.min'\n            },\n            shim: {\n                'brunel' : {\n                    exports: 'BrunelD3',\n                    deps: ['d3', 'topojson'],\n                    init: function() {return {BrunelD3 : BrunelD3,BrunelData : BrunelData}}\n                },\n                'brunelControls' : {\n                    exports: 'BrunelEventHandlers',\n                    init: function() {return {BrunelEventHandlers: BrunelEventHandlers,BrunelJQueryControlFactory: BrunelJQueryControlFactory}}\n                }\n            }\n        }\n    );\n\n    require(\n        [\"d3\"], \n        function(d3) {\n            require(\n                [\"brunel\", \"brunelControls\"], function(brunel, brunelControls) {\nfunction  BrunelVisPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b(visId) {\n  \"use strict\";                                                                       // strict mode\n  var datasets = [],                                      // array of datasets for the original data\n      pre = function(d, i) { return d },                         // default pre-process does nothing\n      post = function(d, i) { return d },                       // default post-process does nothing\n      transitionTime = 200,                                        // transition time for animations\n      charts = [],                                                       // the charts in the system\n      vis = d3.select('#' + visId).attr('class', 'brunel');                     // the SVG container\n\n  BrunelD3.addDefinitions(vis);                                   // ensure standard symbols present\n\n  // Define chart #1 in the visualization //////////////////////////////////////////////////////////\n\n  charts[0] = function(parentNode, filterRows) {\n    var geom = BrunelD3.geometry(parentNode || vis.node(), 0, 0, 1, 1, 0, 28, 44, 28),\n      elements = [];                                              // array of elements in this chart\n    geom.transpose();\n\n    // Define groups for the chart parts ///////////////////////////////////////////////////////////\n\n    var chart =  vis.append('g').attr('class', 'chart1')\n      .attr('transform','translate(' + geom.chart_left + ',' + geom.chart_top + ')');\n    var overlay = chart.append('g').attr('class', 'element').attr('class', 'overlay');\n    var zoom = d3.zoom().scaleExtent([1/3,3]);\n    var zoomNode = overlay.append('rect').attr('class', 'overlay')\n      .attr('x', geom.inner_left).attr('y', geom.inner_top)\n      .attr('width', geom.inner_rawWidth).attr('height', geom.inner_rawHeight)\n      .style('cursor', 'pointer').call(zoom)\n      .node();\n    zoomNode.__zoom = d3.zoomIdentity;\n    chart.append('rect').attr('class', 'background').attr('width', geom.chart_right-geom.chart_left).attr('height', geom.chart_bottom-geom.chart_top);\n    var interior = chart.append('g').attr('class', 'interior zoomNone')\n      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')')\n      .attr('clip-path', 'url(#clip_visualization_chart_PredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b_inner)');\n    interior.append('rect').attr('class', 'inner').attr('width', geom.inner_width).attr('height', geom.inner_height);\n    var gridGroup = interior.append('g').attr('class', 'grid');\n    var axes = chart.append('g').attr('class', 'axis')\n      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')');\n    vis.select('defs').append('clipPath').attr('id', 'clip_visualization_chart_PredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b_inner').append('rect')\n      .attr('x', 0).attr('y', 0)\n      .attr('width', geom.inner_rawWidth+1).attr('height', geom.inner_rawHeight+1);\n\n    // Scales //////////////////////////////////////////////////////////////////////////////////////\n\n    var scale_x = d3.scalePoint().padding(0.5)\n      .domain(['tenure', 'age', 'address', 'employ', 'region', 'longmon', 'income', 'ed', 'longten', 'tollmon', 'lninc', 'cardmon', 'reside', 'loglong', 'cardten', 'equipmon', 'gender', 'marital', 'custcat', 'wiremon', 'equipten', 'callwait', 'wireless', 'forward', 'callid', 'wireten', 'voice', 'tollten', 'internet', 'tollfree', 'confer', 'equip', 'callcard', 'multline', 'pager', 'ebill'])\n      .range([geom.inner_width, 0]);\n    var scale_inner = d3.scaleLinear().domain([0,1])\n      .range([-0.5, 0.5]);\n    var scale_y = d3.scaleLinear().domain([0, 1.0000001])\n      .range([0, geom.inner_height]);\n    var base_scales = [scale_x, scale_y];                           // untransformed original scales\n\n    // Axes ////////////////////////////////////////////////////////////////////////////////////////\n\n    axes.append('g').attr('class', 'x axis')\n      .attr('transform','translate(0,' + geom.inner_rawHeight + ')')\n      .attr('clip-path', 'url(#clip_visualization_chart_PredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b_haxis)')\n      .attr('role', 'region').attr('aria-label', 'Horizontal Axis');\n    vis.select('defs').append('clipPath').attr('id', 'clip_visualization_chart_PredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b_haxis').append('polyline')\n      .attr('points', '-1,-1000, -1,-1 -5,5, -1000,5, -100,1000, 10000,1000 10000,-1000');\n    axes.select('g.axis.x').append('text').attr('class', 'title').text('IMPORTANCE').style('text-anchor', 'middle')\n      .attr('x',geom.inner_rawWidth/2)\n      .attr('dx', 1)\n      .attr('y', geom.inner_bottom - 2.0).attr('dy','-0.27em');\n\n    var axis_bottom = d3.axisBottom(scale_y).ticks(5);\n\n    function buildAxes(time) {\n      var axis_x = axes.select('g.axis.x');\n      BrunelD3.transition(axis_x, time).call(axis_bottom.scale(scale_y));\n    }\n    zoom.on('zoom', function(t, time) {\n        t = t ||BrunelD3.restrictZoom(d3.event.transform, geom, this);\n        scale_x.range([t.y, t.y + t.k * geom.inner_width]);\n        zoomNode.__zoom = t;\n        interior.attr('class', 'interior ' + BrunelD3.zoomLabel(t.k));;\n        build(time || -1);\n    });\n\n    // Define element #1 ///////////////////////////////////////////////////////////////////////////\n\n    elements[0] = function() {\n      var original, processed,                           // data sets passed in and then transformed\n        element, data,                                 // brunel element information and brunel data\n        selection, merged;                                      // d3 selection and merged selection\n      var elementGroup = interior.append('g').attr('class', 'element1')\n        .attr('role', 'region').attr('aria-label', 'Predictor, Importance as bars, also showing selection, sorted by Importance:ascending')\n        .attr('transform','matrix(0,1,1,0,0,0)'),\n        main = elementGroup.append('g').attr('class', 'main'),\n        labels = BrunelD3.undoTransform(elementGroup.append('g').attr('class', 'labels').attr('aria-hidden', 'true'), elementGroup);\n\n      function makeData() {\n        original = datasets[0];\n        if (filterRows) original = original.retainRows(filterRows);\n        processed = pre(original, 0)\n          .sort('Importance:ascending');\n        processed = post(processed, 0);\n        var f0 = processed.field('Predictor'),\n          f1 = processed.field('Importance'),\n          f2 = processed.field('#selection'),\n          f3 = processed.field('#row');\n        var keyFunc = function(d) { return f0.value(d) };\n        data = {\n          Predictor:    function(d) { return f0.value(d.row) },\n          Importance:   function(d) { return f1.value(d.row) },\n          $selection:   function(d) { return f2.value(d.row) },\n          $row:         function(d) { return f3.value(d.row) },\n          Predictor_f:  function(d) { return f0.valueFormatted(d.row) },\n          Importance_f: function(d) { return f1.valueFormatted(d.row) },\n          $selection_f: function(d) { return f2.valueFormatted(d.row) },\n          $row_f:       function(d) { return f3.valueFormatted(d.row) },\n          _split:       function(d) { return f2.value(d.row) },\n          _key:         keyFunc,\n          _rows:        BrunelD3.makeRowsWithKeys(keyFunc, processed.rowCount())\n        };\n      }\n      // Aesthetic Functions\n      var scale_color = d3.scaleOrdinal()\n        .domain(['\\u2717', '\\u2713'])\n        .range([ '#4BBDCB', '#42CBDB']);\n      var color = function(d) { var c = data.$selection(d); return c!=null ? scale_color(c) : null };\n\n      // Build element from data ///////////////////////////////////////////////////////////////////\n\n      function build(transitionMillis) {\n        element = elements[0];\n        var w = 10.0;\n        var x = function(d) { return scale_x(data.Predictor(d))};\n        var h = geom.default_point_size;\n        var y1 = scale_y.range()[0];\n        var y2 = function(d) { return scale_y(data.Importance(d))};\n        var labeling  = [{\n          index: 0, method: 'box', location: ['left', 'top'], inside: false, align: 'end', pad: 1, dy: -0.25,\n          fit: false, granularity: 1,\n          content: function(d) {\n            return d.row == null ? null : data.Predictor_f(d)\n          }\n        }];\n\n        // Define selection entry operations\n        function initialState(selection) {\n          selection\n            .attr('class', 'element bar filled')\n            .attr('role', 'img').attr('aria-label', \n          function(d) { return data.Predictor_f(d);\n          })\n            .each(function(d) {\n              var width = w, left = x(d) - width/2, \n              c = y1, d = y2(d), top = Math.min(c,d), height = Math.max(1e-6, Math.abs(c-d));\n              this.r = {x:left, y:top, w:width, h:height};\n            })\n            .attr('x', function(d) { return this.r.x })\n            .attr('y', function(d) { return this.r.y + this.r.h/2 })\n            .attr('width', function(d) { return this.r.w })\n            .attr('height',0)\n        }\n\n        // Define selection update operations on merged data\n        function updateState(selection) {\n          selection\n            .each(function(d) {\n              var width = w, left = x(d) - width/2, \n              c = y1, d = y2(d), top = Math.min(c,d), height = Math.max(1e-6, Math.abs(c-d));\n              this.r = {x:left, y:top, w:width, h:height};\n            })\n            .attr('x', function(d) { return this.r.x })\n            .attr('y', function(d) { return this.r.y })\n            .attr('width', function(d) { return this.r.w })\n            .attr('height', function(d) { return this.r.h })\n            .filter(BrunelD3.hasData)                     // following only performed for data items\n            .style('fill', color);\n        }\n\n        // Define labeling for the selection\n        function label(selection, transitionMillis) {\n          BrunelD3.label(selection, labels, transitionMillis, geom, labeling);\n        }\n        // Create selections, set the initial state and transition updates\n        selection = main.selectAll('.element').data(data._rows, function(d) { return d.key });\n        var added = selection.enter().append('rect');\n        merged = selection.merge(added);\n        initialState(added);\n        selection.filter(BrunelD3.hasData)\n          .classed('selected', BrunelD3.isSelected(data))\n          .filter(BrunelD3.isSelected(data)).raise();\n        updateState(BrunelD3.transition(merged, transitionMillis));\n        label(merged, transitionMillis);\n\n        selection.exit().each(function() { this.remove(); BrunelD3.removeLabels(this)} );\n        chart.select('rect.overlay')                               // attach handlers to the overlay\n          .on('click.user', function(d) {\n            var c = BrunelD3.closest(merged, 'x', 16 );\n            MdlVizPredImp.showSelected(null, c.target, element, 'xy');\n          })\n          .on('mousemove.user', function(d) {\n            var c = BrunelD3.closest(merged, 'x', 16 );\n            MdlVizPredImp.showTooltip(c.item, c.target, element, 'x');\n          })\n          .on('mouseout.user', function(d) {\n            var c = BrunelD3.closest(merged, 'x', 16 );\n            MdlVizPredImp.showTooltip(null, c.target, element, 'x');\n          });\n        merged                                                     // attach handlers to the element\n          .on('click.user', function(d) {MdlVizPredImp.showSelected(d, this, element) })\n          .on('mouseover.user', function(d) {MdlVizPredImp.showTooltip(d, this, element) })\n          .on('mouseout.user', function(d) {MdlVizPredImp.showTooltip(null, this, element) })\n          .on('mouseover.interact', function(d) {BrunelD3.select(d, this, element, updateAll) })\n          .on('mouseout.interact', function(d) {BrunelD3.select(null, this, element, updateAll) });\n      }\n\n      return {\n        data:           function() { return processed },\n        original:       function() { return original },\n        internal:       function() { return data },\n        selection:      function() { return merged },\n        makeData:       makeData,\n        build:          build,\n        chart:          function() { return charts[0] },\n        group:          function() { return elementGroup },\n        fields: {\n          x:            ['Predictor'],\n          y:            ['Importance'],\n          key:          ['Predictor'],\n          color:        ['#selection']\n        }\n      };\n    }();\n\n    function build(time, noData) {\n      var first = elements[0].data() == null;\n      if (first) time = 0;                                           // no transition for first call\n      buildAxes(time);\n      if ((first || time > -1) && !noData) {\n        elements[0].makeData();\n      }\n      elements[0].build(time);\n    }\n\n    // Expose the following components of the chart\n    return {\n      elements : elements,\n      interior : interior,\n      scales: {x:scale_x, y:scale_y},\n      zoom: function(params, time) {\n          if (params) zoom.on('zoom').call(zoomNode, params, time);\n          return d3.zoomTransform(zoomNode);\n      },\n      build : build\n    };\n    }();\n\n  function setData(rowData, i) { datasets[i||0] = BrunelD3.makeData(rowData) }\n  function updateAll(time) { charts.forEach(function(x) {x.build(time || 0)}) }\n  function buildAll() {\n    for (var i=0;i<arguments.length;i++) setData(arguments[i], i);\n    updateAll(transitionTime);\n  }\n\n  return {\n    dataPreProcess:     function(f) { if (f) pre = f; return pre },\n    dataPostProcess:    function(f) { if (f) post = f; return post },\n    data:               function(d,i) { if (d) setData(d,i); return datasets[i||0] },\n    visId:              visId,\n    build:              buildAll,\n    rebuild:            updateAll,\n    charts:             charts\n  }\n}\n\n// Data Tables /////////////////////////////////////////////////////////////////////////////////////\n\ntablePredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b = {\n   summarized: false,\n   names: ['Predictor', 'Importance'], \n   options: ['string', 'numeric'], \n   rows: [['income', 0.0525565], ['employ', 0.0690745], ['voice', 0.007748], ['callid', 0.0092441],\n  ['gender', 0.0199572], ['equipmon', 0.0202173], ['reside', 0.0284073], ['cardten', 0.0211479],\n  ['wiremon', 0.0172756], ['tollmon', 0.0351872], ['wireten', 0.0088797], ['ebill', 0.0017271],\n  ['callcard', 0.0056158], ['pager', 0.0019532], ['callwait', 0.0146069], ['longmon', 0.0572534],\n  ['custcat', 0.0173991], ['multline', 0.0055596], ['tenure', 0.0982797], ['ed', 0.0441435],\n  ['address', 0.0785334], ['tollten', 0.0069089], ['forward', 0.0128617], ['longten', 0.0427033],\n  ['loglong', 0.0282727], ['tollfree', 0.0064988], ['confer', 0.0058494], ['marital', 0.0186141],\n  ['equip', 0.0057426], ['equipten', 0.0169849], ['cardmon', 0.0333345], ['lninc', 0.0343006],\n  ['wireless', 0.0128718], ['region', 0.0609191], ['age', 0.0926797], ['internet', 0.0066909]]\n};\n\n// Call Code to Build the system ///////////////////////////////////////////////////////////////////\n\nvPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b = new BrunelVisPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b('visualizationPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b');\nBrunelD3.animateBuild(vPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b, tablePredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b, 700);\n\nvar chart = vPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b.charts[0], scx = chart.scales.x, range = scx.range(), nCats = scx.domain().length;\npiChartVarName = vPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b;\ncurrentSelected = nCats-1;\nselected = [nCats-1];\nfunction modifySelection(data) {\n\tvar i;\n\tvar field = data.field('#selection');\n\tfor (i=0; i<selected.length; i++) {\n\t\tfield.setValue('\u2713', selected[i]);\n\t}\n\treturn data;\n}\nvPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b.dataPostProcess(modifySelection);\nMdlVizPredImp.init(); \ncurrentSelectedKey =  $( \".interactionTooltip\").attr(\"data-predictor\");\nvar width = Math.abs(range[range.length-1] - range[0]);\nvar desiredGap = 40;\nif (nCats <= 3) desiredGap = 140;\nelse if (nCats <= 6) desiredGap = 70;\nelse if (nCats <= 9) desiredGap = 46;\nvar desiredRange = desiredGap * nCats;\nchart.zoom(d3.zoomIdentity.scale(desiredRange/width));\nvPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b.build();\n\nfunction  hideInteractiveControls() {\n\t$('.interactionContainer').css('data-width-adj', 0);\n\t$('.interactionContainer').css('data-height-adj', 0);\n\t$('.otherChartZoom').hide();\n}\n\n\t               }\n            );\n        }\n    );\n                </script>\n              </div>\n\n<div class=\"interactivity\">\n<div class=\"PIThinScroll\" onscroll=\"MdlVizPanZoom.piScroll(vPredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b,this)\" id=\"PredictorImportanceJS_6ab88938_2dfe_47a0_a8b4_9b752e28731b_scrollDiv\"><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br />\n</div>\n<div class=\"interaction\">\n\t<div class=\"interactionTooltip\" data-predictor=\"tenure\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"tenure\">tenure</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.09827966941065328\">.098</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"35.52599999999991\">35.526</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"21.349129349929118\">21.349</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>1.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>72.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"age\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"age\">age</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.09267974650077622\">.093</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"41.68400000000005\">41.684</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"12.552535361431792\">12.553</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>18.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>77.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"address\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"address\">address</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.07853336422945445\">.079</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"11.551000000000027\">11.551</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"10.081636722278745\">10.082</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>55.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"employ\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"employ\">employ</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.06907448682009665\">.069</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"10.987000000000018\">10.987</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"10.07704475528413\">10.077</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>47.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"region\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"region\">region</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.060919071091450026\">.061</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>2</td><td>334</td><td>33.4</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>3</td><td>344</td><td>34.4</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>322</td><td>32.2</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"longmon\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"longmon\">longmon</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.05725337060971731\">.057</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"11.723100000000024\">11.723</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"10.358303258256141\">10.358</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>.900</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>99.950</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"income\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"income\">income</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.05255653566072008\">.053</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"77.53500000000007\">77.535</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"106.99062937939908\">106.991</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>9.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>1,668.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"ed\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"ed\">ed</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.0441435104369965\">.044</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>204</td><td>20.4</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>2</td><td>287</td><td>28.7</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>3</td><td>209</td><td>20.9</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>4</td><td>234</td><td>23.4</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>5</td><td>66</td><td>6.6</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"longten\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"longten\">longten</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.04270327508331043\">.043</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"574.0500499999978\">574.050</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"789.5792601648676\">789.579</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>.900</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>7,257.600</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"tollmon\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"tollmon\">tollmon</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.035187185542016344\">.035</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"13.273999999999996\">13.274</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"16.893668902876016\">16.894</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>173.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"lninc\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"lninc\">lninc</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.03430063366392936\">.034</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"3.957203333786304\">3.957</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"0.803351935876095\">.803</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td data-num=\"2.19722457733622\">2.197</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td data-num=\"7.41938058291869\">7.419</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"cardmon\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"cardmon\">cardmon</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.03333452632043775\">.033</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"13.781000000000002\">13.781</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"14.077452326326675\">14.077</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>109.250</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"reside\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"reside\">reside</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.028407253823376624\">.028</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"2.331000000000006\">2.331</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"1.435074562522786\">1.435</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>1.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>8.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"loglong\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"loglong\">loglong</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.028272724261203783\">.028</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"2.182109944115337\">2.182</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"0.734184460443918\">.734</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td data-num=\"-0.105360515657826\">-.105</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td data-num=\"4.60467006094641\">4.605</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"cardten\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"cardten\">cardten</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.021147928378622807\">.021</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"605.7737500000005\">605.774</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"829.7109025880261\">829.711</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>7,515.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"equipmon\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"equipmon\">equipmon</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.02021730962441059\">.020</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"14.219800000000003\">14.220</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"19.05900215016516\">19.059</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>77.700</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"gender\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"gender\">gender</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.0199571962385639\">.020</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>483</td><td>48.3</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>517</td><td>51.7</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"marital\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"marital\">marital</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.01861408332413184\">.019</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>495</td><td>49.5</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>505</td><td>50.5</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"custcat\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"custcat\">custcat</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.017399090055918182\">.017</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>266</td><td>26.6</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>4</td><td>236</td><td>23.6</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>3</td><td>281</td><td>28.1</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>2</td><td>217</td><td>21.7</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"wiremon\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"wiremon\">wiremon</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.017275550153555072\">.017</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"11.583899999999996\">11.584</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"19.709563434789725\">19.710</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>111.950</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"equipten\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"equipten\">equipten</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.016984944939168894\">.017</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"465.6328500000002\">465.633</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"856.8443270824499\">856.844</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>5,028.650</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"callwait\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"callwait\">callwait</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.014606916064862167\">.015</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>515</td><td>51.5</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>485</td><td>48.5</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"wireless\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"wireless\">wireless</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.012871795128155323\">.013</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>704</td><td>70.4</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>296</td><td>29.6</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"forward\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"forward\">forward</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.012861703903650651\">.013</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>493</td><td>49.3</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>507</td><td>50.7</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"callid\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"callid\">callid</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.009244142243798026\">.009</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>519</td><td>51.9</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>481</td><td>48.1</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"wireten\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"wireten\">wireten</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.008879701891621168\">.009</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"442.73689999999993\">442.737</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"970.9854072041402\">970.985</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>7,856.850</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"voice\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"voice\">voice</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.0077479532404481095\">.008</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>696</td><td>69.6</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>304</td><td>30.4</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"tollten\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"tollten\">tollten</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.0069089392261299145\">.007</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Mean</th><td data-num=\"551.2584999999993\">551.258</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Standard Deviation</th><td data-num=\"915.2887453381855\">915.289</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Minimum</th><td>.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">Maximum</th><td>5,916.000</td></tr>\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><th class=\"mdlvr-table-tbody-tr-th mdlvr-table-stringHeader\">N</th><td>1,000</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"internet\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"internet\">internet</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.006690863176462053\">.007</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>632</td><td>63.2</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>368</td><td>36.8</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"tollfree\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"tollfree\">tollfree</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.006498843243405101\">.006</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>526</td><td>52.6</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>474</td><td>47.4</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"confer\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"confer\">confer</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.005849391624010458\">.006</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>498</td><td>49.8</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>502</td><td>50.2</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"equip\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"equip\">equip</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.005742594593768687\">.006</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>614</td><td>61.4</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>386</td><td>38.6</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"callcard\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"callcard\">callcard</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.005615773535047639\">.006</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>678</td><td>67.8</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>322</td><td>32.2</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"multline\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"multline\">multline</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.005559619645360631\">.006</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>525</td><td>52.5</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>475</td><td>47.5</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"pager\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"pager\">pager</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.0019532051844218185\">.002</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>739</td><td>73.9</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>261</td><td>26.1</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n\t<div class=\"interactionTooltip\" data-predictor=\"ebill\">\n\t\t<div class=\"PITooltip\">\n\t\t\t<p class=\"piPredictor\" title=\"ebill\">ebill</p>\n\t\t\t<p class=\"piLabel\">Importance</p>\n\t\t\t<p class=\"piImportance\" title=\"0.001727101130348389\">.002</p>\n\t\t</div>\n\t\t<div class=\"PITable\">\n\t\t\t<div class=\"ptableScroll\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\">\n\t\t\t\t<thead class=\"mdlvr-table-thead\">\n\t\t\t\t\t<tr class=\"mdlvr-table-thead-tr\"><th class=\"mdlvr-table-thead-tr-th\">Value</th><th class=\"mdlvr-table-tbody-tr-th\">Count</th><th class=\"mdlvr-table-tbody-tr-th\">Percent</th></tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody class=\"mdlvr-table-tbody\">\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>0</td><td>629</td><td>62.9</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td>1</td><td>371</td><td>37.1</td></tr>\n\t\t\t\t</tbody>\n\t\t\t</table>\n\t\t</div>\n\t\t</div>\n\t</div>\n</div>\n\n</div>\n            </div>\n          </div>\n\n        </div>\n      </div>\n\n      <div class=\"modelVisualizationDiv modelVisualizationDiv_6ab88938_2dfe_47a0_a8b4_9b752e28731b\" id=\"modelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_6\" style=\"display: none;\">\n        <div id=\"tableDiv_6ab88938_2dfe_47a0_a8b4_9b752e28731b_6\" class=\"tableDiv vDiv\" data-t1=\"Top Decision Rules\" data-id=\"spss.randomTrees.table.TopDecisionRules\" data-t2=\"Target : churn\" data-t3=\"<div class='MVInfotooltip'><a ondblclick='MdlVizTable.printTable(modelVisualization_6ab88938_2dfe_47a0_a8b4_9b752e28731b_6)' class='infodot' style='background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/1.0.0/images/carbon/info_small_18.svg);'><span class='MVInfotooltiptext'>Shown only for classification models when special handling of imbalanced data is not in effect, this table displays the most \u201cinteresting\u201d decision rules. Interesting decision rules are defined as those which have high prediction accuracy and high agreement with the predictions of the random trees ensemble. The table includes rule and ensemble accuracy figures, as well as the Interestingness Index.</span><a></div>\">\n\t\t\t<table class=\"mdlvr-table cell-border hover\" data-id=\"Top_Decision_Rules\" id=\"Top_Decision_Rules_6ab88938_2dfe_47a0_a8b4_9b752e28731b\"><caption>Top Decision Rules</caption><thead class=\"OriginalHeader\"><tr><th tabindex=\"0\">Decision Rule</th><th tabindex=\"0\">Most Frequent Category</th><th tabindex=\"0\">Rule Accuracy</th><th tabindex=\"0\">Ensemble Accuracy</th><th tabindex=\"0\">Interestingness Index</th></tr></thead><tbody class=\"mdlvr-table-body\"><tr><th data-meta=\"0\" tabindex=\"0\"><ul class='collapsibleList'><li tabindex='0'>gender = 1</li></ul><ul class='collapsibleList'><li tabindex='0'>longmon > 19.3</li></ul><ul class='collapsibleList'><li tabindex='0'>longmon > 10.95</li></ul></th><td tabindex=\"0\" class=\"string\">0</td><td tabindex=\"0\">1.000</td><td tabindex=\"0\">1.000</td><td tabindex=\"0\">1.000</td></tr><tr><th data-meta=\"0\" tabindex=\"0\"><ul class='collapsibleList'><li tabindex='0'>tenure > 52.0</li></ul><ul class='collapsibleList'><li tabindex='0'>internet = 0</li></ul><ul class='collapsibleList'><li tabindex='0'>income <= 72.0</li></ul><ul class='collapsibleList'><li tabindex='0'>cardmon > 0.0</li></ul><ul class='collapsibleList'><li tabindex='0'>equipmon <= 0.0</li></ul></th><td tabindex=\"0\" class=\"string\">0</td><td tabindex=\"0\">0.975</td><td tabindex=\"0\">1.000</td><td tabindex=\"0\" data-num=\"0.950625\">0.951</td></tr><tr><th data-meta=\"0\" tabindex=\"0\"><ul class='collapsibleList'><li tabindex='0'>gender = 1</li></ul><ul class='collapsibleList'><li tabindex='0'>custcat = <ul class='collapsibleList'><li tabindex='0'>1</li><li tabindex='0'>3</li><li tabindex='0'>2</li></ul></li></ul><ul class='collapsibleList'><li tabindex='0'>loglong > 2.61006979274201</li></ul><ul class='collapsibleList'><li tabindex='0'>equipmon <= 0.0</li></ul></th><td tabindex=\"0\" class=\"string\">0</td><td tabindex=\"0\" data-num=\"0.967741935483871\">0.968</td><td tabindex=\"0\">1.000</td><td tabindex=\"0\" data-num=\"0.936524453694069\">0.937</td></tr><tr><th data-meta=\"0\" tabindex=\"0\"><ul class='collapsibleList'><li tabindex='0'>tenure > 52.0</li></ul><ul class='collapsibleList'><li tabindex='0'>lninc <= 5.04342511691925</li></ul><ul class='collapsibleList'><li tabindex='0'>address > 8.0</li></ul><ul class='collapsibleList'><li tabindex='0'>ebill = 0</li></ul><ul class='collapsibleList'><li tabindex='0'>tenure > 30.0</li></ul></th><td tabindex=\"0\" class=\"string\">0</td><td tabindex=\"0\">0.950</td><td tabindex=\"0\">0.950</td><td tabindex=\"0\" data-num=\"0.9025\">0.902</td></tr><tr><th data-meta=\"0\" tabindex=\"0\"><ul class='collapsibleList'><li tabindex='0'>wireless = 0</li></ul><ul class='collapsibleList'><li tabindex='0'>age > 49.0</li></ul><ul class='collapsibleList'><li tabindex='0'>address > 5.0</li></ul><ul class='collapsibleList'><li tabindex='0'>callcard = 1</li></ul><ul class='collapsibleList'><li tabindex='0'>tenure > 30.0</li></ul></th><td tabindex=\"0\" class=\"string\">0</td><td tabindex=\"0\" data-num=\"0.921568627450981\">0.922</td><td tabindex=\"0\" data-num=\"0.980392156862745\">0.980</td><td tabindex=\"0\" data-num=\"0.850351674695253\">0.850</td></tr></tbody></table>\n        </div>\n      </div>\n\n    </div>\n  </body>\n</html>\n</div>\n"}, "execution_count": 11, "metadata": {}}], "execution_count": 11}, {"source": "### 4.3. Export the XML files (PMML, StatXML) for other detail statistics\nBy exporting your results to different formats, such as Predictive Model Markup Language (PMML) or statXML format you can share your statistical analyses outside of IBM Data Science Experience.", "cell_type": "markdown", "metadata": {"collapsed": true}}, {"source": "import java.io.{File, PrintWriter}\n\nsrfModel.toPMML(\"randomTrees_pmml.xml\")\nval statXML = srfModel.statXML()\nnew PrintWriter(\"StatXML.xml\") {\n      write(statXML)\n      close\n}", "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "execute_result", "data": {"text/plain": "$anon$1@5f613d99"}, "execution_count": 12, "metadata": {}}], "execution_count": 12}, {"source": "<a id=\"next\"></a>\n# Summary and next steps\nYou have created a predictive model of churn rate by using IBM SPSS Algorithm on Apache Spark. Now you can create a different model to compare model evaluations, such as the test of model effects, residuals, and so on. See [SPSS documentation](https://apsportal.ibm.com/docs/content/kc_gen/integrations-gen2.html).", "cell_type": "markdown", "metadata": {"collapsed": true}}, {"source": "## Authors\n\nWang Zhiyuan and Yu Wenpei are SPSS Algorithm Engineers at IBM.", "cell_type": "markdown", "metadata": {}}, {"source": "Copyright \u00a9 2017 IBM. This notebook and its source code are released under the terms of the MIT License.", "cell_type": "markdown", "metadata": {"collapsed": true}}], "metadata": {"kernelspec": {"display_name": "Scala 2.11 with Spark 2.1", "name": "scala-spark21", "language": "scala"}, "widgets": {"state": {}, "version": "1.1.2"}, "language_info": {"mimetype": "text/x-scala", "version": "2.11.8", "name": "scala", "pygments_lexer": "scala", "file_extension": ".scala", "codemirror_mode": "text/x-scala"}}, "nbformat": 4}