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:
- Find the reduction in mean squared errors when the predictor splits a node.
- 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
| Term | Description |
|---|---|
| yi | 
observed response value |
 | mean response |
 | 
fitted response |
| N | number of rows |
