Interpretable cost-sensitive regression through one-step boosting

Thomas Decorte, Jakob Raymaekers, Tim Verdonck

Abstract: Highlights•A One-Step Boosting algorithm is proposed for post hoc cost-sensitivity in regression•The boosting step uses a linear function as a secondary learner for cost-sensitivity•The method consistently yields a significant reduction in average misprediction cost•The obtained results become interpretable through bootstrapping