Methods and formulas for the model summary in MARS® Regression


This command is available with the Predictive Analytics Module. Click here for more information about how to activate the module.

Select the method or formula of your choice.

Important predictors

The number of predictors with positive relative importance.

A MARS® Regression model comes from a sequence of basis functions that improve the model. The importance scores come from the optimal model that the analysis identifies. To calculate the importance score, the analysis finds the change in the mean squared error when all basis functions for a predictor are removed from the optimal model and the reduced model is refit to the training data. The relative importance compares the importance score for a predictor to the maximum importance score.


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 observed response value
mean response
fitted response
Nnumber of rows