Overview for CART® Classification

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

CART® Classification 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 market researcher can use CART® Classification to identify customers that have higher response rates to specific initiatives and to predict those response rates.

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

Where to find this analysis

To perform a CART® Classification, choose Stat > Predictive Analytics > CART® Classification.

When to use an alternate analysis

If you have a continuous response variable, use CART® Regression.

To try to improve the fit of the tree, Minitab offers TreeNet® Classification and Random Forests® Classification analyses with the Predictive Analytics Module. Click here for more information about how to activate the module.

1 Breiman, Friedman, Olshen & Stone. (1984). Classification and Regression Trees. Boca Raton, Florida: Chapman & Hall/CRC.
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