CART is a robust, easy-to-use decision tree tool that automatically sifts large, complex databases, searching for and isolating significant patterns and relationships. This discovered knowledge is then used to generate reliable, easy-to-grasp predictive models for applications such as profiling customers, targeting direct mailings, detecting telecommunications and credit card fraud and managing credit risk.

CART's Proven Methodology

Read the details on the proven methodology implemented by CART.

CART 6.0

CART is an acronym for Classification and Regression Trees, a decision-tree procedure introduced in 1984 by world-renowned UC Berkeley and Stanford statisticians, Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone.

CART uses an intuitive, Windows based interface, making it accessible to both technical and non technical users. Underlying the "easy" interface, however, is a mature theoretical foundation that distinguishes CART from other methodologies and other decision trees. CART is the only decision tree system based on the original CART code developed by world renowned Stanford University and University of California at Berkeley statisticians; this code now includes enhancements that were co-developed by Salford Systems and CART's originators.

Based on a decade of machine learning and statistical research, CART provides stable performance and reliable results.

In addition, CART is an excellent pre-processing complement to other data analysis techniques. For example, CART's outputs (predicted values) can be used as inputs to improve the predictive accuracy of neural nets and logistic regression. NEW TreeCoder Model Deployment Module
TreeCoder is an add-on module for deploying CART models directly in SAS -- quickly and accurately.

The decision logic of a CART tree, including the surrogate rules utilised if primary splitting values are missing, is automatically implemented. The resulting source code can be dropped into a SAS run without modification thus eliminating errors due to hand coding of decision rules and enabling fast and accurate model deployment.


Resources

CART FAQ

Various questions and answers about Decision Trees within CART.

CART's Proven Methodology

Details on the proven methodology implemented by CART.

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