What does the Variable Selection tool do with R-square criterion?

The Variable Selection tool is designed to identify which predictor variables in the dataset are most relevant for building a predictive model. When using the R-square criterion, it evaluates the contribution of each variable in explaining the variability in the target variable. The tool analyzes how much additional explanatory power each variable brings to the model, and those that provide significant improvements in R-square are retained while less influential variables may be excluded. This helps streamline the model by keeping only the most impactful variables, ultimately enhancing model performance and interpretability.

This process not only enhances efficiency in modeling but also avoids issues related to overfitting, where too many variables lead to unnecessary complexity without a corresponding benefit to model accuracy. Thus, the primary function of the tool focuses on selecting variables based on their contribution to R-square, making it integral to the modeling process in SAS Enterprise Miner.