Methods and formulas for the model summary in Fit Model and Discover Key Predictors with TreeNet® Regression


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Select the method or formula of your choice.

Important predictors

The number of predictors with positive relative importance.
A TreeNet® Regression model comes from a sequence of small regression trees that use generalized residuals as the response variable. The calculation of the model improvement score for a predictor from a single tree has two steps:
  1. Find the reduction in mean squared errors when the predictor splits a node.
  2. Add all the reductions from all the nodes where the predictor is the node splitter.

Then, the importance score for the predictor equals the sum of the model improvement scores across all the trees.


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
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