Model selection refers to a set of exploratory tools for improving regression models. Each model selection technique involves selecting a subset of possible predictor variables that still account well for the variation in the regression model's observation variable. These tools are often helpful for problems in which the designer wants the simplest possible explanation for variation in the observation variable, or wants to maximize the chance of obtaining good parameter values for a regression model.
The model selection tools available in Analytics.h++ include forward, backward, stepwise, and exhaustive selection for both linear and logistic regression models.
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