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

Note

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 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.

R-squared

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)

Notation

TermDescription
yi observed response value
mean response
fitted response
Nnumber of rows
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