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Advanced Predictive Modeling Using IBM SPSS Modeler

Overview

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.

Prerequisites

Introduction to IBM SPSS Modeler and Data Mining or some experience analyzing data with IBM SPSS Modeler
Classifying Customers Using IBM SPSS Modeler and Predicting Continuous Targets Using IBM SPSS Modeler, or some experience with predictive models in IBM SPSS Modeler

Additional Course

The course Clustering and Association Models Using IBM SPSS Modeler is suggested.

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