General Classification/Regression Trees Button

Click the button to display the General Classification/Regression Trees Startup Panel. Classification and Regression Trees are used to classify (divide) cases based on a set of predictor variables. Unlike linear or nonlinear regression-like algorithms, this module will find hierarchical decision rules to provide optimal separation between observations with regard to a categorical or continuous criterion variable, based on splits on one or more continuous and/or categorical predictor variables. This module is a comprehensive implementation of the methods described as CART® by Breiman, Friedman, Olshen, and Stone (1984). However, the General Trees module contains various extensions and options that are typically not found in implementations of this algorithm, and that are particularly useful for data mining applications. In addition to standard analyses, the implementation of these methods in STATISTICA allows you to specify ANOVA/ANCOVA-like designs with continuous and/or categorical predictor variables, and their interactions.

Please consult the product description brochure for sales information concerning this module, and specifically, which modules are included in each STATISTICA product.