Methods and formulas for the model summary in CART® Regression

Select the method or formula of your choice.

Important predictors

The number of predictors with positive relative importance.

Any regression tree is a collection of splits. Each split provides improvement to the tree. Each split also includes surrogate splits that also provide improvement to the tree. The importance of a variable is given by all of its improvements when the tree uses the variable to split a node or as a surrogate to split a node when another variable has a missing value. The following formula gives the improvement at a single node:

The values of I(t), pLeft, and pRight depend on the criterion for splitting the nodes. For more information, go to Node splitting methods in CART® Regression.

The formula for the relative importance for the qth predictor scales the importance by the most important variable:


R2 is also known as the coefficient of determination.

Root mean squared error (RMSE)

Mean squared error (MSE)

Mean absolute deviation (MAD)

Mean absolute percent error (MAPE)


yi i th observed response value
mean response
i th fitted response
Nnumber of records