Advanced Predictive Modeling Using IBM SPSS Modeler (V18.2)

This course builds on the courses Classifying Customers Using IBM SPSS Modeler (V16) and Predicting Continuous Targets Using IBM SPSS Modeler (V16). 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.

1. Preparing Data for Modeling
1. Addressing general data quality issues
2. Handling anomalies
3. Selecting important predictors
4. Partitioning the data to better evaluate models
5. Balancing the data to build better models
2. Reducing Data with PCA/Factor
1. Explain the basic ideas behind PCA/Factor
2. Customize two options in the PCA/Factor node
3. Using Decision List to Create Rulesets
1. Explain how Decision List builds a ruleset
2. Using Decision List interactively
3. Creating rulesets directly with Decision List
4. Advanced Predictive Models
1. Explain the basic ideas behind SVM
2. Customizing two options in the SVM node
3. Explain the basic ideas behind Bayes Net
4. Customizing two options in the SVM node
5. Combining Models
1. Using the Ensemble node to combine model predictions
2. Improving the model performance by meta-level modeling
6. Finding the Best Predictive Model
1. Find the best model for categorical targets
2. Find the best model for continuous targets