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

When you choose
Discover Key
Predictors
from the
TreeNet^{®}
Classification
menu, you can specify how to eliminate the terms.

- Method
- Choose whether to eliminate the least important or most important
predictors first.
- Eliminate unimportant predictors
- Eliminate the least important predictors first to select a subset of predictors to use for the model. For example, to reduce a set of 500 predictors to the 10 most important predictors. The algorithm removes the least important predictors sequentially, shows you results that let you compare models with different numbers of predictors, and produces results for the set of predictors with the best value of the model selection criterion.
- Eliminate important predictors to assess their impacts
- Eliminate the most important predictors first to assess the effect on the model. For example, use this option to see the change in the average –loglikelihood value as the most important predictors leave the model. The algorithm removes the most important predictors sequentially, shows you results that let you evaluate the effect of each important predictor on the accuracy criterion, and produces results for the model with all the predictors.

- Eliminate K predictors at each step
- Usually, you eliminate 1 predictor at a time. If you have an extremely large number of predictors and you expect few predictors are very important, consider a larger value. For example, you can remove more predictors per step and increase the maximum number of elimination steps to remove more predictors faster.
- Maximum number of elimination steps
- Usually, the maximum number of elimination steps is the number of reduced models you want to examine, but the algorithm stops early if the model runs out of predictors. When you increase the number, you usually eliminate a small number of predictors at each step relative to the number of predictors and want to continue so that you can see smaller models. For example, you can remove more predictors per step and increase the maximum number of elimination steps to remove more predictors faster. Decrease this value to evaluate fewer alternative models.
- Specify predictors to be removed last
- Specify a subset of predictors to remove after the rest of the predictors. For example, You have 10 predictors and specify 3 predictors to remove last. The algorithm removes the other 7 predictors before it considers any of the 3 predictors that you specify. Usually, you specify predictors to remove last when you have a special interest in one or more predictors. For example, you can specify the predictors to remove last so that the algorithm evaluates a model with only those predictors.
- Display model selection table
- Choose whether to display the results for the training data.
- For test set
- Usually, you display the results for the test set. The algorithm uses these results to determine which variables to eliminate. The test results indicate whether the model can adequately predict the response values for new observations, or properly summarize the relationships between the response and the predictor variables.
- For test and training sets
- The training results are usually more ideal than the actual results for new data. The training results are for reference only.