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.
Introduction to advanced machine learning models
Overview of models to create natural groupings
Factor and component scores
Assess model fit
Introduction to Generalized Linear Models
Available link functions
Use external machine learning programs in IBM SPSS Modeler
Modeling with text data
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