Use CART® Classification to create a decision tree for a binomial or multinomial categorical response with many categorical and continuous predictor variables.

Partition and classify data with CART (Classification & Regression Trees), one of the most powerful modern tools for data mining. Optimize data association and find hidden trends that …

Complete the following steps to interpret CART® Regression. Key output includes the tree diagram, R 2, variable importance, and the residual plots.

SPM’s CART ® modeling engine is the ultimate classification tree that has revolutionized the field of advanced analytics, and inaugurated the current era of data science.

Open the sample data set LengthOfService.MWX. Choose Predictive Analytics Module > CART® Regression. In Response, enter Length of Service. In Continuous predictors, enter Age at …

CART is a decision tree algorithm that works by creating a set of yes or no rules that splits the target or outcome variable into partitions based on the predictor or input settings.

CART® Regression illustrates important patterns and relationships between a continuous response and important predictors within highly complicated data, without using parametric …

CART supports "high-level categorical variables" through its proprietary algorithms that quickly determine effective splits in spite of the daunting combinatorics of many-valued predictors.

Use the Gain and Lift charts to assess the performance of your classification model. The Gain chart plots the total positive rate in percent versus the percent of total counts. So, for example, …

Welcome to CART®, a robust decision-tree tool for data mining, predictive modeling, and data preprocessing. CART automatically searches for important patterns and relationships, …