Advanced Predictive Modeling Using IBM SPSS Modeler (V18)
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 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.
This intermediate-level course is for users of IBM SPSS Modeler responsible for building predictive models (also known as classification models).
You should have:
- Completion of the course Introduction to IBM SPSS Modeler and Data Mining (V18) or experience in analyzing data with IBM SPSS Modeler.
- Familiarity with basic modeling techniques, either through completion of the courses Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (V18) and Predictive Modeling for Continuous Targets Using IBM SPSS Modeler (V18), or by experience with predictive models in IBM SPSS Modeler.
Preparing Data for Modeling
Reducing Data with PCA/Factor
Using Decision List to Create Rulesets
Advanced Predictive Models
Finding the Best Predictive Model