Predicting churn with the SPSS random tree algorithm.ipynb

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            "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)    ", 
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            "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", 
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            "metadata": {}
        }, 
        {
            "execution_count": 2, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "ename": "SyntaxError", 
                    "evalue": "invalid syntax (<ipython-input-2-145b05dae8f6>, line 1)", 
                    "traceback": [
                        "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-2-145b05dae8f6>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    val link_telco = \"https://raw.githubusercontent.com/AlgorithmDemo/SampleData/master/telco.csv\"\u001b[0m\n\u001b[0m                 ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
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                    "output_type": "error"
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            "source": "val link_telco = \"https://raw.githubusercontent.com/AlgorithmDemo/SampleData/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_Feb = \"https://raw.githubusercontent.com/AlgorithmDemo/SampleData/master/telco_Feb.csv\"\n\nimport sys.process._\nimport java.net.URL\nimport java.io.File\nnew URL(link_telco_Feb) #> new File(\"telco_Feb.csv\") !!"
        }, 
        {
            "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": {}
        }, 
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                    "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|     2|    13| 44|      1|      9|    64|  4|     5|     0|     0|     2|       0|    0|       1|       0|    3.7|    0.0|     0.0|    7.5|    0.0|  37.45|    0.0|     0.0|  110.0|    0.0|       0|    0|    0|       0|     0|       0|      1|     0|    0|1.30833281965018|       |       |2.01490302054226|       |4.15888308335967|      1|    1|\n+------+------+---+-------+-------+------+---+------+------+------+------+--------+-----+--------+--------+-------+-------+--------+-------+-------+-------+-------+--------+-------+-------+--------+-----+-----+--------+------+--------+-------+------+-----+----------------+-------+-------+----------------+-------+----------------+-------+-----+\nonly showing top 1 row\n\n"
                }
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            "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(1)"
        }, 
        {
            "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
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            "execution_count": 3, 
            "cell_type": "code", 
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            "outputs": [
                {
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                    "text": "root\n |-- region: integer (nullable = true)\n |-- tenure: integer (nullable = true)\n |-- age: integer (nullable = true)\n |-- marital: integer (nullable = true)\n |-- address: integer (nullable = true)\n |-- income: integer (nullable = true)\n |-- ed: integer (nullable = true)\n |-- employ: integer (nullable = true)\n |-- retire: integer (nullable = true)\n |-- gender: integer (nullable = true)\n |-- reside: integer (nullable = true)\n |-- tollfree: integer (nullable = true)\n |-- equip: integer (nullable = true)\n |-- callcard: integer (nullable = true)\n |-- wireless: integer (nullable = true)\n |-- longmon: double (nullable = true)\n |-- tollmon: double (nullable = true)\n |-- equipmon: double (nullable = true)\n |-- cardmon: double (nullable = true)\n |-- wiremon: double (nullable = true)\n |-- longten: double (nullable = true)\n |-- tollten: double (nullable = true)\n |-- equipten: double (nullable = true)\n |-- cardten: double (nullable = true)\n |-- wireten: double (nullable = true)\n |-- multline: integer (nullable = true)\n |-- voice: integer (nullable = true)\n |-- pager: integer (nullable = true)\n |-- internet: integer (nullable = true)\n |-- callid: integer (nullable = true)\n |-- callwait: integer (nullable = true)\n |-- forward: integer (nullable = true)\n |-- confer: integer (nullable = true)\n |-- ebill: integer (nullable = true)\n |-- loglong: double (nullable = true)\n |-- logtoll: string (nullable = true)\n |-- logequi: string (nullable = true)\n |-- logcard: string (nullable = true)\n |-- logwire: string (nullable = true)\n |-- lninc: double (nullable = true)\n |-- custcat: integer (nullable = true)\n |-- churn: integer (nullable = true)\n\n"
                }
            ], 
            "source": "dfTelco.printSchema"
        }, 
        {
            "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
            }
        }, 
        {
            "execution_count": 4, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "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\")"
        }, 
        {
            "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": {}
        }, 
        {
            "execution_count": 5, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "import com.ibm.spss.ml.classificationandregression.ensemble.RandomTrees\nimport com.ibm.spss.ml.utils.DataFrameImplicits.DataFrameEnrichImplicitsClass\n\nval ordinal = Array(\"ed\")\nval nominal = Array(\"region\",\n     \"marital\",\n     \"retire\",\n     \"gender\",\n     \"tollfree\",\n     \"equip\",\n     \"callcard\",\n     \"wireless\",\"multline\",\n     \"voice\",\"pager\",\"internet\",\"callid\",\"callwait\",\"forward\",\"confer\",\n     \"ebill\",\n     \"custcat\",\n     \"churn\"\n   )\nval srf = RandomTrees().setTargetField(\"churn\").setNumTrees(10)\nval srfModel = srf.fit(dfTelco.setNominalMeasure(nominal,true).setOrdinalMeasure(ordinal,true))"
        }, 
        {
            "source": "Do the prediction and get your results.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 6, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "val predResult = srfModel.transform(dfTelcoFeb)\nval predResultNew = predResult.withColumn(\"prediction\", predResult(\"prediction\").cast(\"double\")).\n    withColumn(\"churn\", predResult(\"churn\").cast(\"double\"))"
        }, 
        {
            "source": "To get the accuracy result, use the Apache Spark **MulticlassClassificationEvaluator** function. Notice that the accuracy is above 90%.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 7, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "acc_result:0.942\n"
                }
            ], 
            "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\")"
        }, 
        {
            "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": {}
        }, 
        {
            "execution_count": 8, 
            "cell_type": "code", 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "source": ""
        }, 
        {
            "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": {}
        }, 
        {
            "execution_count": 9, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 9, 
                    "metadata": {}, 
                    "data": {
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document.getElementsByClassName('loading');\n        for(var i = 0; i < vl.length; i++) {\n          vl[i].style.display = 'none';\n        }\n\n        $('#modelVisualization_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_1').fadeIn('slow');\n\n        MdlVizTopDecisionRules.addDropdowns();\n      });\n    </script>\n  </head>\n  <body style=\"overflow: auto;\" data-model-viewer-uuid=\"_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" data-model=\"spss.randomTrees\" data-caller=\"dsx-notebook\">\n\n\n    <div id=\"left_pane_div_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" class=\"mdlvr left_pane_div\">\n      <div class=\"leftTitle\">Random Trees\n\t\t<div class='MVInfotooltip'>\n\t\t\t<a class='infodot' style='background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/images/light/info_small_18.svg);'></a>\n\t\t\t\t<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. The IBM SPSS Spark Machine Learning Library implementation features a table of top decision rules for classification models without imbalance handling and measures of relative predictor importance for all models.<br /><br />For more information, visit the <a target=\"_blank\" href=\"https://datascience.ibm.com/docs/content/analyze-data/spss-viz-random.html\">Random trees page</a> on the Data Science Experience web site.</span>\n\t\t</div>\n      </div>\n      <hr class=\"leftSectionSplitter\">\n      <ul class=\"menuList\">\n      <li class=\"menuList\"><a class=\"showModelVisualizationMenuItem_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb active\" id=\"showModelVisualization_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_1\" data-mv-type=\"table\">Model Information</a></li>\n      <li class=\"menuList\"><a class=\"showModelVisualizationMenuItem_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" id=\"showModelVisualization_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_2\" data-mv-type=\"table\">Records Summary</a></li>\n      <li class=\"menuList\"><a class=\"showModelVisualizationMenuItem_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" id=\"showModelVisualization_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_3\" data-mv-type=\"chart\">Predictor Importance</a></li>\n      <li class=\"menuList\"><a class=\"showModelVisualizationMenuItem_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" id=\"showModelVisualization_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_4\" data-mv-type=\"table\">Top Decision Rules</a></li>\n      <li class=\"menuList\"><a class=\"showModelVisualizationMenuItem_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" id=\"showModelVisualization_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_5\" data-mv-type=\"table\">Confusion Matrix</a></li>\n      </ul>    </div>\n\n    <div id=\"right_pane_div_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" class=\"mdlvr right_pane_div\" style=\"width: 60%;\">\n      <div class=\"vTitle1\" id=\"vTitle1_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\">&nbsp;</div>\n      <div class=\"vTitle2\" id=\"vTitle2_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\">&nbsp;</div>\n      <hr class=\"sectionSplitter\">\n<div class=\"bearerTokenWarningMessage\" style=\"display:none\">\n        <button class=\"alert__close\" onclick=\"closeAlert(this)\">\n            <svg class=\"icon--24 icon--close\" xmlns=\"https://www.w3.org/2000/svg\" viewbox=\"0 0 24 24\"><circle fill=\"none\" cx=\"11.9\" cy=\"12\" r=\"10\"/><path class=\"icon--close-x\" d=\"M15.2 7.6l-3.3 3.3-3.4-3.3-1.1 1.1 3.4 3.3-3.4 3.4 1.1 1.1 3.4-3.4 3.3 3.4 1.1-1.1L13 12l3.3-3.3\"/><path fill=\"#A6276E\" d=\"M11.9 1C5.8 1 .9 6 .9 12s4.9 11 11 11 11-5 11-11-5-11-11-11zm0 20.5c-5.2 0-9.4-4.2-9.4-9.4s4.2-9.4 9.4-9.4 9.4 4.2 9.4 9.4-4.2 9.4-9.4 9.4z\"/></svg>\n        </button>\n        <svg class=\"icon--24 icon--warning\" xmlns=\"https://www.w3.org/2000/svg\" viewbox=\"0 0 24 24\">\n    <path fill=\"#FDD600\" d=\"M1 22L12 3l11 19\"></path><path fill=\"#1D3649\" d=\"M11 17h2v2h-2zM11 11h2v4h-2z\"></path></svg>\n        <span class=\"project_token\">Missing Project Token -</span>The toHTML method now\n        requires a ProjectContext parameter as the first parameter. For more information, refer to the <a href=\"https://datascience.ibm.com/docs/content/analyze-data/token.html\" target=\"_blank\">project token</a> documentation page.            This API will cease to work without a ProjectContext\n        parameter in the near future.\n    </div>\n      <div class=\"titleBuffer\"></div>\n\n      <div class=\"modelVisualizationDiv modelVisualizationDiv_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" id=\"modelVisualization_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_1\" style=\"\">\n        <div class=\"tableDiv vDiv\" data-t1=\"Model Information\" data-id=\"spss.randomTrees.table.ModelInformation\" data-t2=\"Target : churn\" data-t3=\"<div class='MVInfotooltip'><a class='infodot' style='background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/images/light/info_small_18.svg);'><span class='MVInfotooltiptext'>Shows model settings and other input, and provides summary measures to help you assess the model.</span></div>\">\n<div>\n\n<table data-id=\"Model_Information\" id=\"Model_Information_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" class=\"mdlvr-table cell-border hover\">\n<tbody class=\"mdlvr-table-body\">\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\">Target Field</th>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td mdlvr-table-string\">churn</td></tr>\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\">Model Building Method</th>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td mdlvr-table-string\">Random Trees Classification</td></tr>\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\">Number of Predictors Input</th>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">36</td></tr>\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\">Model Accuracy</th>\n<td data-num=\"0.695257315842583\" tabindex=\"0\" class=\"mdlvr-table-tbody-td\">0.695</td></tr>\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\">Misclassification Rate</th>\n<td data-num=\"0.304742684157417\" tabindex=\"0\" class=\"mdlvr-table-tbody-td\">0.305</td></tr></tbody></table>\n</div>\n\n        </div>\n      </div>\n\n      <div class=\"modelVisualizationDiv modelVisualizationDiv_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" id=\"modelVisualization_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_2\" style=\"display: none;\">\n        <div class=\"tableDiv vDiv\" data-t1=\"Records Summary\" data-id=\"spss.randomTrees.table.RecordsSummary\" data-t2=\"Target : churn\" data-t3=\"<div class='MVInfotooltip'><a class='infodot' style='background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/images/light/info_small_18.svg);'><span class='MVInfotooltiptext'>Shows the number and percentage of records included and excluded from the analysis.</span></div>\">\n<div>\n<script>\n    var Records_Summary_tableInitSettings = '\"scrollX\":\"100%\",\"scrollY\":\"60vh\",\"scrollCollapse\":true,\"ordering\":false,\"searching\":false,\"paging\":false,\"info\":false,\"bFilter\":false';\n    if ( $('#Records_Summary_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb').length && $('#Records_Summary_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb').hasClass('dataTablesInitialized') == false ) {\n      initDataTablesTable('#Records_Summary_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb', Records_Summary_tableInitSettings);\n    }\n    $('#Records_Summary_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb').addClass('dataTablesInitialized');\n</script>\n\n<table data-id=\"Records_Summary\" id=\"Records_Summary_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" class=\"mdlvr-table cell-border hover\">\n<thead class=\"OriginalHeader mdlvr-table-head\">\n<tr class=\"mdlvr-table-thead-tr\">\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-thead-tr-th\">Records</th>\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-thead-tr-th\">Number</th>\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-thead-tr-th\">Percent</th></tr></thead>\n<tbody class=\"mdlvr-table-body\">\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\">Included</th>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">1,000</td>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">100.00</td></tr>\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\">Excluded</th>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">0</td>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">0.00</td></tr>\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\">Total</th>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">1,000</td>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">100.00</td></tr></tbody></table>\n</div>\n\n        </div>\n      </div>\n\n      <div class=\"modelVisualizationDiv modelVisualizationDiv_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" id=\"modelVisualization_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_3\" 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 class='infodot' style='background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/images/light/info_small_18.svg);'><span class='MVInfotooltiptext'>Shows the relative importance of each predictor in estimating the model.</span></div>\">\n\n<!-- ***************** Notebook Calling =  TRUE; width=540 ; formatOptions.getImageSizeX() = 680; NOT Defined *************** -->\n<div class=\"interactionContainer\">\n<div class=\"interactionSubContainer\">\n<div class=\"interactionChart\" id=\"PredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\">\n\t<style>\n\t\t#visualizationPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb.brunel .chart1 .label {\n\tfont-size: 12px;\n\tlabel-location: outside-top-left;\n\tpadding: 1px;\n\ttext-align: end;\n}\n\n#visualizationPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb.brunel .chart1 .element.bar {\n\twidth: 4px;\n}\n\n#visualizationPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb.brunel .chart1 .axis .tick line {\n\tsize: 8px;\n\tstroke-width: 1px;\n}\n\n#visualizationPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb.brunel .chart1 .axis.y .title {\n\tlabel-location: left;\n\tpadding-left: 5px;\n}\n\n#visualizationPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb.brunel .chart1 .axis.x .title {\n\tlabel-location: left;\n\tpadding-bottom: 5px;\n}\n\n#visualizationPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb.brunel .chart1 .element.bar.selected {\n\tfill: #db2780;\n}\n\n#visualizationPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb.brunel .chart1 .axis.y .tick text {\n\tfont-size: 12px;\n\tpadding-left: 15px;\n\tpadding-right: 5px;\n}\n\n#visualizationPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb.brunel .chart1 .axis.x .tick text {\n\tfont-size: 12px;\n\tpadding-bottom: 15px;\n\tpadding-top: 5px;\n}\n\t</style>\n<svg id=\"visualizationPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" data-name=\"PredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" width=\"427\" height=\"440\"></svg>\n<script>\nvar vPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb;\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.2.3.min',\n                'brunelControls' : 'https://model-viewer.mybluemix.net/mdlvr/assets/3PP/javascripts/brunel/brunel.controls.2.3.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_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb(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, 25, 44, 25),\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_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_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_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_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_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_haxis)')\n      .attr('role', 'region').attr('aria-label', 'Horizontal Axis');\n    vis.select('defs').append('clipPath').attr('id', 'clip_visualization_chart_PredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_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', 'start')\n      .attr('x',0)\n      .attr('dx', 5)\n      .attr('y', geom.inner_bottom - 2.0).attr('dy','-0.27em');\n\n    var axis_bottom = d3.axisBottom(scale_y).ticks(Math.min(10, Math.round(geom.inner_width / 69.0)));\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          $row_f:       function(d) { return f3.valueFormatted(d.row) },\n          _split:       function(d) { return f2.value(d.row) },\n          _key:         keyFunc,\n          _rows:        BrunelD3.makeRowsWithKeys(keyFunc, processed.rowCount())\n        };\n      }\n      // Aesthetic Functions\n      var scale_color = d3.scaleOrdinal()\n        .domain(['&#10007;', '&#10003;'])\n        .range([ '#C15993', '#E662A3']);\n      var color = function(d) { return scale_color(data.$selection(d)) };\n\n      // Build element from data ///////////////////////////////////////////////////////////////////\n\n      function build(transitionMillis) {\n        element = elements[0];\n        var w = 4.0;\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        }\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        BrunelD3.transition(selection.exit(), transitionMillis/3)\n          .style('opacity', 0.5).each( function() {\n            this.remove(); BrunelD3.removeLabels(this); \n        });\n        chart.select('rect.overlay')                               // attach handlers to the overlay\n          .on('click.user', function(d) {\n            var c = BrunelD3.closest(merged, 'x', 13 );\n            MdlVizPredImp.showSelected(null, c.target, element, 'xy');\n          })\n          .on('mousemove.user', function(d) {\n            var c = BrunelD3.closest(merged, 'x', 13 );\n            MdlVizPredImp.showTooltip(c.item, c.target, element, 'x');\n          })\n          .on('mouseout.user', function(d) {\n            var c = BrunelD3.closest(merged, 'x', 13 );\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\nvar tablePredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb = {\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_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb = new BrunelVisPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb('visualizationPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb');\nBrunelD3.animateBuild(vPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb, tablePredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb, 700);\n\nvar chart = vPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb.charts[0], scx = chart.scales.x, range = scx.range(), nCats = scx.domain().length;\npiChartVarName = vPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb;\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('&#10003;', selected[i]);\n\t}\n\treturn data;\n}\nvPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb.dataPostProcess(modifySelection);\nMdlVizPredImp.init(); \ncurrentSelectedKey =  $( \".interactionTooltip\").attr(\"data-predictor\");\nvar width = Math.abs(range[range.length-1] - range[0]);\nvar desiredGap = 30;\nif (nCats <= 3) desiredGap = 140;\nelse if (nCats <= 6) desiredGap = 70;\nelse if (nCats <= 9) desiredGap = 46;\nvar desiredRange = desiredGap * nCats;\nchart.zoom(d3.zoomIdentity.scale(desiredRange/width));\nvPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb.build();\n\nfunction  hideInteractiveControls() {\n\t$('.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=\"PIThinScroll\" onscroll=\"MdlVizPanZoom.piScroll(vPredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb,this)\" id=\"PredictorImportanceJS_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">2</td><td class=\"mdlvr-table-tbody-td\">334</td><td class=\"mdlvr-table-tbody-td\">33.4</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">3</td><td class=\"mdlvr-table-tbody-td\">344</td><td class=\"mdlvr-table-tbody-td\">34.4</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">322</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">204</td><td class=\"mdlvr-table-tbody-td\">20.4</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">2</td><td class=\"mdlvr-table-tbody-td\">287</td><td class=\"mdlvr-table-tbody-td\">28.7</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">3</td><td class=\"mdlvr-table-tbody-td\">209</td><td class=\"mdlvr-table-tbody-td\">20.9</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">4</td><td class=\"mdlvr-table-tbody-td\">234</td><td class=\"mdlvr-table-tbody-td\">23.4</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">5</td><td class=\"mdlvr-table-tbody-td\">66</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">483</td><td class=\"mdlvr-table-tbody-td\">48.3</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">517</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">495</td><td class=\"mdlvr-table-tbody-td\">49.5</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">505</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">266</td><td class=\"mdlvr-table-tbody-td\">26.6</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">4</td><td class=\"mdlvr-table-tbody-td\">236</td><td class=\"mdlvr-table-tbody-td\">23.6</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">3</td><td class=\"mdlvr-table-tbody-td\">281</td><td class=\"mdlvr-table-tbody-td\">28.1</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">2</td><td class=\"mdlvr-table-tbody-td\">217</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">515</td><td class=\"mdlvr-table-tbody-td\">51.5</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">485</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">704</td><td class=\"mdlvr-table-tbody-td\">70.4</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">296</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">493</td><td class=\"mdlvr-table-tbody-td\">49.3</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">507</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">519</td><td class=\"mdlvr-table-tbody-td\">51.9</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">481</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">696</td><td class=\"mdlvr-table-tbody-td\">69.6</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">304</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">632</td><td class=\"mdlvr-table-tbody-td\">63.2</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">368</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">526</td><td class=\"mdlvr-table-tbody-td\">52.6</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">474</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">498</td><td class=\"mdlvr-table-tbody-td\">49.8</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">502</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">614</td><td class=\"mdlvr-table-tbody-td\">61.4</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">386</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">678</td><td class=\"mdlvr-table-tbody-td\">67.8</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">322</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">525</td><td class=\"mdlvr-table-tbody-td\">52.5</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">475</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">739</td><td class=\"mdlvr-table-tbody-td\">73.9</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">261</td><td class=\"mdlvr-table-tbody-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 class=\"mdlvr-table-tbody-td\">0</td><td class=\"mdlvr-table-tbody-td\">629</td><td class=\"mdlvr-table-tbody-td\">62.9</td></tr>\n\n\t\t\t\t\t<tr class=\"mdlvr-table-tbody-tr\"><td class=\"mdlvr-table-tbody-td\">1</td><td class=\"mdlvr-table-tbody-td\">371</td><td class=\"mdlvr-table-tbody-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</div>\n</div>\n\n        </div>\n      </div>\n\n      <div class=\"modelVisualizationDiv modelVisualizationDiv_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" id=\"modelVisualization_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_4\" style=\"display: none;\">\n        <div class=\"tableDiv vDiv\" data-t1=\"Top Decision Rules\" data-id=\"spss.randomTrees.table.TopDecisionRules\" data-t2=\"Target : churn\" data-t3=\"<div class='MVInfotooltip'><a class='infodot' style='background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/images/light/info_small_18.svg);'><span class='MVInfotooltiptext'>The decision rules leading to the terminal or leaf nodes with the highest percentages of the total records. Rules are defined by lists of conditions that define the partitioning of data by the algorithm and can be used to assign individual records to child nodes based on the values of different predictors.</span></div>\">\n<div>\n\n<table data-id=\"Top_Decision_Rules\" id=\"Top_Decision_Rules_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" class=\"mdlvr-table cell-border hover\">\n<thead class=\"OriginalHeader mdlvr-table-head\">\n<tr class=\"mdlvr-table-thead-tr\">\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-thead-tr-th\">Decision Rule</th>\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-thead-tr-th\">Most Frequent Category</th>\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-thead-tr-th\">Rule Accuracy</th>\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-thead-tr-th\">Ensemble Accuracy</th>\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-thead-tr-th\">Interestingness Index</th></tr></thead>\n<tbody class=\"mdlvr-table-body\">\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" data-meta=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\"><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>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td mdlvr-table-string\">0</td>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">1.000</td>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">1.000</td>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">1.000</td></tr>\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" data-meta=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\"><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>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td mdlvr-table-string\">0</td>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">0.975</td>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">1.000</td>\n<td data-num=\"0.950625\" tabindex=\"0\" class=\"mdlvr-table-tbody-td\">0.951</td></tr>\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" data-meta=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\"><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>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td mdlvr-table-string\">0</td>\n<td data-num=\"0.967741935483871\" tabindex=\"0\" class=\"mdlvr-table-tbody-td\">0.968</td>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">1.000</td>\n<td data-num=\"0.936524453694069\" tabindex=\"0\" class=\"mdlvr-table-tbody-td\">0.937</td></tr>\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" data-meta=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\"><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>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td mdlvr-table-string\">0</td>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">0.950</td>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">0.950</td>\n<td data-num=\"0.9025\" tabindex=\"0\" class=\"mdlvr-table-tbody-td\">0.902</td></tr>\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" data-meta=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\"><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>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td mdlvr-table-string\">0</td>\n<td data-num=\"0.921568627450981\" tabindex=\"0\" class=\"mdlvr-table-tbody-td\">0.922</td>\n<td data-num=\"0.980392156862745\" tabindex=\"0\" class=\"mdlvr-table-tbody-td\">0.980</td>\n<td data-num=\"0.850351674695253\" tabindex=\"0\" class=\"mdlvr-table-tbody-td\">0.850</td></tr></tbody></table>\n</div>\n\n        </div>\n      </div>\n\n      <div class=\"modelVisualizationDiv modelVisualizationDiv_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" id=\"modelVisualization_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb_5\" style=\"display: none;\">\n        <div class=\"tableDiv vDiv\" data-t1=\"Confusion Matrix\" data-id=\"spss.randomTrees.table.ConfusionMatrix\" data-t2=\"Target : churn\" data-t3=\"<div class='MVInfotooltip'><a class='infodot' style='background-image: url(https://model-viewer.mybluemix.net/mdlvr/assets/images/light/info_small_18.svg);'><span class='MVInfotooltiptext'>Shows numbers and proportions of correct and incorrect classifications for appropriate models.</span></div>\">\n<div>\n<script>\n    var Confusion_Matrix_tableInitSettings = '\"scrollX\":\"100%\",\"scrollY\":\"60vh\",\"scrollCollapse\":true,\"ordering\":false,\"searching\":false,\"paging\":false,\"info\":false,\"bFilter\":false';\n    if ( $('#Confusion_Matrix_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb').length && $('#Confusion_Matrix_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb').hasClass('dataTablesInitialized') == false ) {\n      initDataTablesTable('#Confusion_Matrix_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb', Confusion_Matrix_tableInitSettings);\n    }\n    $('#Confusion_Matrix_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb').addClass('dataTablesInitialized');\n</script>\n\n<table data-id=\"Confusion_Matrix\" id=\"Confusion_Matrix_be58db2e_d7b9_4d84_a4cd_c3f2a68fcceb\" class=\"mdlvr-table cell-border hover\">\n<thead class=\"OriginalHeader mdlvr-table-head\">\n<tr class=\"mdlvr-table-thead-tr\">\n<th rowspan=\"2\" tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-thead-tr-th\">Observed</th>\n<th colspan=\"3\" tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-thead-tr-th\">Predicted</th></tr>\n<tr class=\"mdlvr-table-thead-tr\">\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-thead-tr-th\">1</th>\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-thead-tr-th\">0</th>\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-thead-tr-th\">Percent Correct</th></tr></thead>\n<tbody class=\"mdlvr-table-body\">\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\">1</th>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">119</td>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">153</td>\n<td data-num=\"43.75\" tabindex=\"0\" class=\"mdlvr-table-tbody-td\">43.8%</td></tr>\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\">0</th>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">149</td>\n<td tabindex=\"0\" class=\"mdlvr-table-tbody-td\">570</td>\n<td data-num=\"79.27677329624478\" tabindex=\"0\" class=\"mdlvr-table-tbody-td\">79.3%</td></tr>\n<tr class=\"mdlvr-table-tbody-tr\">\n<th tabindex=\"0\" class=\"mdlvr-table-stringHeader mdlvr-table-tbody-tr-th\">Percent Correct</th>\n<td data-num=\"44.40298507462687\" tabindex=\"0\" class=\"mdlvr-table-tbody-td\">44.4%</td>\n<td data-num=\"78.83817427385893\" tabindex=\"0\" class=\"mdlvr-table-tbody-td\">78.8%</td>\n<td data-num=\"69.52573158425832\" tabindex=\"0\" class=\"mdlvr-table-tbody-td\">69.5%</td></tr></tbody></table>\n</div>\n\n        </div>\n      </div>\n\n    </div>\n  </body>\n</html>\n</div>\n"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "import com.ibm.spss.scala.ModelViewer\nkernel.magics.html(ModelViewer.toHTML(pc,srfModel))"
        }, 
        {
            "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": {
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            }
        }, 
        {
            "execution_count": 10, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 10, 
                    "metadata": {}, 
                    "data": {
                        "text/plain": "$anon$1@59740911"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "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}"
        }, 
        {
            "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.", 
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        }, 
        {
            "source": "Copyright \u00a9 2017 IBM. This notebook and its source code are released under the terms of the MIT License.", 
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