This course builds on the courses Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (v18) and Predictive Modeling for Continuous Targets Using IBM SPSS Modeler (v18). It presents advanced techniques to predict categorical and continuous targets. Before reviewing the modeling techniques, data preparation issues are addressed such as partitioning and detecting anomalies. Also, a method to reduce the number of fields to a number of core fields, referred to as components or factors, is presented. Advanced classification models, such as Decision List, Support Vector Machines and Bayes Net, are reviewed. Methods are presented to combine individual models into a single model in order to improve predictive power, including running and evaluating many models in a single run, both for categorical and continuous targets.
• General computer literacy • 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 basic knowledge of modeling. • Prior completion of Introduction to Predictive Models using IBM SPSS Modeler (v18) is recommended. • Familiarity with basic modeling techniques, either through completion of the courses Predictive Modeling for Categorical Targets Using IBM SPSS Modeler and/or Predictive Modeling for Continuous Targets Using IBM SPSS Modeler, or by experience with predictive models in IBM SPSS Modeler.
Users of IBM SPSS Modeler responsible for building predictive models who want to leverage the full potential of classification models in IBM SPSS Modeler.