Overview for CART® Regression

Use CART® Regression to create an decision tree for a continuous response with many categorical or continuous predictors. CART® Regression illustrates important patterns and relationships between a continuous response and important predictors within highly complicated data, without using parametric methods.

CART® Regression provides insights for a wide range of applications, including manufacturing quality control, drug discovery, fraud detection, credit scoring, and churn prediction. Use the results to identify important variables, to identify groups in the data with desirable characteristics, and to predict response values for new observations. For example, a bank manager wants to identify potential customers that have higher response rates to specific initiatives.

CART® Regression is the only decision-tree methodology that uses the original code from world-renowned professors from Stanford University and University of California at Berkeley. While decision trees with various algorithms are popular tools, the CART® methodology of producing decision trees distinguishes itself through its features and performance. The CART® methodology remains proprietary and includes enhancements from decades of experience with practical applications.

For a more complete introduction to the CART® methodology, see Breiman, Friedman, Olshen and Stone (1984)1.

Where to find this analysis

To create a classification tree, choose Stat > Predictive Analytics > CART® Regression.

When to use an alternate analysis

If you have a categorical response variable, use CART® Classification.

1 Breiman, Friedman, Olshen & Stone. (1984). Classification and Regression Trees. Boca Raton, Florida: Chapman & Hall/CRC.
By using this site you agree to the use of cookies for analytics and personalized content.  Read our policy