This course teaches a number of advanced modeling techniques to predict categorical and continuous targets, and is meant for users of IBM SPSS Modeler responsible for building predictive (or "classification") models. Before reviewing the various modeling techniques, prerequisites for building successful models are addressed. The next lessons discuss Decision List, Support Vector Machines and Bayes Nets. After having discussed individual models, it is demonstrated how multiple models can be combined to improve the predictive power. Finally, the focus is on how to automate the process of finding the best predictive model. Each lesson is accompanied by demonstrations and learning activities, to acquire hands-on experience.
This advanced course is for users of IBM SPSS Modeler responsible for building predictive (or "classification") models
There are no follow-ons for this course.
Preparing Data for Modeling
Rule Induction using Decision List
Machine Learning Models
Combining Predictive Models
Finding the Best Predictive Model
Data Reduction using PCA/Factor