Relative variable importance chart for Fit Model and Discover Key Predictors with TreeNet® Regression


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

The Relative variable importance graph plots the predictors in order of their effect on model improvement when splits are made on a predictor over the sequence of trees. The variable with the highest improvement score is set as the most important variable, and the other variables follow in order of importance. Relative variable importance standardizes the importance values for ease of interpretation. Relative importance is defined as the percent improvement with respect to the most important predictor, which has an importance of 100%.

Relative importance is calculated by dividing each variable importance score by the largest importance score of the variables, then we multiply by 100%.


Relative variable importance values range from 0% to 100%. The most important variable always has a relative importance of 100%. If a variable is not used in the model at all, it is not important.

The most important predictor variable is Core Based Statistical Area. If the importance of the top predictor variable, Core Based Statistical Area, is 100%, then the next important variable, Annual Income, has a contribution of 92.8%. This means the annual income of the borrower is 92.8% as important as the geographical location of the property.