SplitWise regression for capturing nonlinear effects in interpretable model selection.

ABSTRACT: Capturing nonlinear relationships while maintaining interpretability remains a persistent challenge in regression modeling. We introduce SplitWise, a stepwise regression framework that adaptively transforms numeric predictors into threshold-based binary features using shallow decision trees-only when such transformations improve model fit according to the Akaike or Bayesian Information Criterion. This design preserves the transparency of linear models while flexibly capturing threshold-based nonlinear effects, positioning SplitWise between classical linear and interpretable nonlinear regression. SplitWise retains a single, globally linear equation that selectively incorporates data-driven thresholds-yielding models that remain straightforward to interpret and verify. Across synthetic scena

SUBMITTER: Kurbucz MT 

PROVIDER: S-EPMC12660875 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature