i IBM SPSS Decision Trees 21 Note: Before using this information and the product it supports, read the general information under Notices on p. 104. This edition applies to IBM® SPSS® Statistics 21 and to all subsequent releases and modifications until otherwise indicated in new editions. Adobe product screenshot(s) reprinted with permission from Adobe Systems Incorporated. Microsoft product screenshot(s) reprinted with permission from Microsoft Corporation. Licensed Materials - Property of IBM © Copyright IBM Corporation 1989, 2012. U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Preface IBM® SPSS® Statistics is a comprehensive system for analyzing data. The Decision Trees optional add-on module provides the additional analytic techniques described in this manual. The Decision Trees add-on module must be used with the SPSS Statistics Core system and is completely integrated into that system. 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For more information on these seminars, go to http://www.ibm.com/software/analytics/spss/training. iv Contents Part I: User's Guide 1 Creating Decision Trees 1 SelectingCategories.......................................................... 6 Validation................................................................... 7 Tree-GrowingCriteria.......................................................... 8 GrowthLimits............................................................ 9 CHAIDCriteria............................................................10 CRTCriteria..............................................................12 QUESTCriteria............................................................13 PruningTrees............................................................14 Surrogates..............................................................15 Options.....................................................................15 MisclassificationCosts.....................................................16 Profits..................................................................17 Prior Probabilities . 18 Scores..................................................................20 MissingValues...........................................................21 SavingModelInformation.......................................................22 Output.....................................................................23 TreeDisplay..............................................................24 Statistics................................................................26 Charts..................................................................29 SelectionandScoringRules.................................................35 2 Tree Editor 37 Working with LargeTrees.......................................................38 TreeMap................................................................39 ScalingtheTreeDisplay....................................................39 NodeSummaryWindow....................................................40 Controlling Information Displayed in the Tree . 41 ChangingTreeColorsandTextFonts...............................................42 CaseSelectionandScoringRules................................................44 FilteringCases............................................................44 SavingSelectionandScoringRules............................................45 © Copyright IBM Corporation 1989, 2012. v Part II: Examples 3 Data assumptions and requirements 48 Effectsofmeasurementlevelontreemodels........................................48 Permanentlyassigningmeasurementlevel......................................51 Variableswithanunknownmeasurementlevel...................................52 Effectsofvaluelabelsontreemodels..............................................52 Assigningvaluelabelstoallvalues............................................54 4 Using Decision Trees to Evaluate Credit Risk 55 Creating the Model............................................................55 Building the CHAIDTreeModel...............................................55 SelectingTargetCategories..................................................56 SpecifyingTreeGrowingCriteria..............................................57 Selecting AdditionalOutput..................................................58 SavingPredictedValues....................................................60 Evaluating theModel..........................................................61 Model Summary Table......................................................62 TreeDiagram.............................................................63 TreeTable...............................................................64 Gains for Nodes...........................................................65 GainsChart..............................................................66 IndexChart..............................................................66 Risk Estimate andClassification...............................................67 PredictedValues..........................................................68 Refining the Model............................................................69 Selecting CasesinNodes...................................................69 ExaminingtheSelectedCases................................................70 AssigningCoststoOutcomes.................................................72 Summary...................................................................76 5 Building a Scoring Model 77 BuildingtheModel............................................................77 EvaluatingtheModel..........................................................78 ModelSummary..........................................................79 vi TreeModelDiagram.......................................................80 RiskEstimate.............................................................81 ApplyingtheModeltoAnotherDataFile............................................82 Summary...................................................................85 6 Missing Values in Tree Models 86 MissingValueswithCHAID.....................................................86 CHAIDResults............................................................88 MissingValueswithCRT........................................................89 CRTResults..............................................................92 Summary...................................................................94 Appendices A Sample Files 95 B Notices 104 Index 107 vii Part I: User's Guide Chapter 1 Creating Decision Trees Figure 1-1 Decision tree The Decision Tree procedure creates a tree-based classification model. It classifies cases into groups or predicts values of a dependent (target) variable based on values of independent (predictor) variables. The procedure provides validation tools for exploratory and confirmatory classification analysis. The procedure can be used for: Segmentation. Identify persons who are likely to be members of a particular group. Stratification. Assign cases into one of several