The predictor elimination analysis first builds a model with all the
predictors. The analysis uses that model to calculate the importance scores for
all the predictors. The next steps depend on whether the analysis eliminates
unimportant predictors or important predictors.
- Unimportant predictors
- The analysis begins by sorting the predictors in order by their
importance scores. If the analysis eliminates unimportant predictors, then the
analysis builds sequential models by removing the least important predictors in
the sorted list. More specifically, at each model step, the analysis removes
any predictors with importance scores of 0 plus the specified number of least
important predictors. The analysis builds a model with this subset of the
predictors. The analysis uses that model to recalculate the importance values
for all the predictors that remain in the analysis. The steps of predictor
elimination and model construction repeat for the maximum number of elimination
steps.
- Important predictors
- The analysis begins by sorting the predictors in order by their
importance scores. If the analysis eliminates important predictors, then the
analysis builds sequential models by removing the most important predictors in
the sorted list. More specifically, at each model step, the analysis builds a
model without the most important predictor from the previous model. The
analysis uses that model to recalculate the importance scores for all the
predictors that remain in the analysis. The steps of predictor elimination and
model construction repeat for the maximum number of elimination steps.
For details on the calculation of the model summary statistics in the Model
Selection table, go to
Methods and formulas for the model summary in Fit Model and Discover Key Predictors with TreeNet® Regression.
