How to Select Variables Robustly in a Scoring Model

A method for robust variable selection in scoring models is presented, emphasizing stability across data subsets over simple performance. The process uses stratified cross-validation to create four data folds, and a variable is only kept if it passes selection criteria on every single fold. A four-rule filter method is applied sequentially, using statistical tests like the Kruskal-Wallis test and Cramér’s V to eliminate variables that are uncorrelated with the target or redundant with other variables. This approach ensures the final set of variables is stable, interpretable, and less likely to fail on new, unseen data.