Discriminant Node Model Options

Model name. You can generate the model name automatically based on the target or ID field (or model type in cases where no such field is specified) or specify a custom name.

Use partitioned data. If a partition field is defined, this option ensures that data from only the training partition is used to build the model. 

Create split models. Builds a separate model for each possible value of input fields that are specified as split fields. See Building Split Models for more information.

Method. The following options are available for entering predictors into the model:

• Enter. This is the default method, which enters all of the terms into the equation directly. Terms that do not add significantly to the predictive power of the model are not added.
• Stepwise. The initial model is the simplest model possible, with no model terms (except the constant) in the equation. At each step, terms that have not yet been added to the model are evaluated, and if the best of those terms adds significantly to the predictive power of the model, it is added.

Note: The Stepwise method has a strong tendency to overfit the training data. When using these methods, it is especially important to verify the validity of the resulting model with a hold-out test sample or new data.