This course presents advanced models available in IBM SPSS Modeler. The participant is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core factors, referred to as components or factors. The next topics focus on supervised models, including Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed on how to analyze text data, combine individual models into a single model, and how to enhance the power of IBM SPSS Modeler by adding external models, developed in Python or R, to the Modeling palette.
1. Introduction to advanced machine learning models
2. Group fields: Factor Analysis and Principal Component Analysis
3. Predict targets with Nearest Neighbor Analysis
4. Explore advanced supervised models
5. Introduction to Generalized Linear Models
6. Combine supervised models
7. Use external machine learning models
8. Analyze text data