This course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing, pass/fail exams, and machine break-down. Students are introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. Students will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.
Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and a basic knowledge of modeling.Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1) is recommended.
Analytics business users who have completed the Introduction to IBM SPSS Modeler and Data Mining course and who want to become familiar with analytical models to predict a categorical field (yes/no churn, yes/no fraud, yes/no response to a mailing, pass/fail exams, yes/no machine break-down, and so forth).
1: Introduction to predictive models for categorical targetsIdentify three modeling objectivesExplain the concept of field measurement level and its implications for selecting a modeling techniqueList three types of models to predict categorical targets� �2: Building decision trees interactively with CHAIDExplain how CHAID grows decision treesBuild a customized model with CHAIDEvaluate a model by means of accuracy, risk, response and gainUse the model nugget to score records� �3: Building decision trees interactively with C&R Tree and QuestExplain how C&R Tree grows a treeExplain how Quest grows a treeBuild a customized model using C&R Tree and QuestList two differences between CHAID, C&R Tree, and Quest�4: Building decision trees directlyCustomize two options in the CHAID nodeCustomize two options in the C&R Tree nodeCustomize two options in the Quest nodeCustomize two options in the C5.0 nodeUse the Analysis node and Evaluation node to evaluate and compare modelsList two differences between CHAID, C&R Tree, Quest, and C5.0� �5: Using traditional statistical modelsExplain key concepts for DiscriminantCustomize one option in the Discriminant nodeExplain key concepts for LogisticCustomize one option in the Logistic node� �6: Using machine learning modelsExplain key concepts for Neural NetCustomize one option in the Neural Net nodeCloud & Data PlatformData and AIInformation Architecture MarketAI Tools & Runtime PortfolioDecision Optimization