Understanding and Applying Tree-Based Methods for Predictor Screening and Modeling

Learn to use JMP and JMP Pro tree-based modeling methods to segment predictors into groups that can be summarized in the form of a tree. See how to use these non-linear methods for regression and classification, easily interpret the results, and enhance the models using bagging, random forest and boosting techniques to create multiple trees that can further increase prediction accuracy.

This webcast covers: Building and tuning models using Partition, Gradient Boosting, Boosted Tree, and Bootstrap Forest; and using Column Contributions to identify key factors.