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Advanced Predictive Modeling Using IBM SPSS Modeler



This course builds on the courses Classifying Customers Using IBM SPSS Modeler and Predicting Continuous Targets Using IBM SPSS Modeler. 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 as components or factors, is presented. The next two modules focus on advanced predictive models, such as Decision List, Support Vector Machines and Bayes Net. Following this presentation, two modules present methods 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.


Additional Course

Key Topics

Preparing Data for Modeling

  • Addressing general data quality issues
  • Handling anomalies
  • Selecting important predictors
  • Partitioning the data to better evaluate models
  • Balancing the data to build better models

Reducing Data with PCA/Factor

  • Explain the basic ideas behind PCA/Factor
  • Customize options in the PCA/Factor node

Using Decision List to Create Rulesets

  • Explain how Decision List builds a ruleset
  • Using Decision List interactively
  • Creating rulesets directly with Decision List

Advanced Predictive Models

  • Explain the basic ideas behind SVM
  • Customizing options in the SVM node
  • Explain the basic ideas behind Bayes Net
  • Customizing options in the Bayes Net node

Combining Models

  • Using the Ensemble node to combine model predictions
  • Improving the model performance by meta-level modeling

Finding the Best Predictive Model

  • Find the best model for categorical targets
  • Find the best model for continuous targets

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