Weight of Evidence Coding and Binning of Predictors in Logistic Regression

Weight of evidence (WOE) coding of a nominal or discrete variable is widely used when preparing predictors for usage in binary logistic regression models. When using WOE coding, an important preliminary step is binning of the levels of the predictor to achieve parsimony without giving up predictive power. These concepts of WOE and binning are extended to ordinal logistic regression in the case of the cumulative logit model. SAS® code to perform binning in the binary case and in the ordinal case is discussed. Lastly, guidelines for assignment of degrees of freedom for WOE-coded predictors within a fitted logistic model are discussed. The assignment of degrees of freedom bears on the ranking of logistic models by SBC (Schwarz Bayes). All computations in this talk are performed by using SAS® and SAS/STAT®.