AI RESEARCH
FlagGAM: Rule-Based Generalized Additive Modeling for Explainable Tabular Prediction
arXiv CS.LG
•
ArXi:2605.31189v1 Announce Type: new Tabular prediction in high-stakes domains requires models that are accurate, transparent, and robust to imperfect inputs. We propose FlagGAM, a rule-defined basis framework that separates feature-level rule construction from prediction. A Flag Core Module converts numerical and categorical variables into sparse, human-readable univariate bases, including threshold flags, category-level flags, tail-deviation bases, and categorical step functions; a default additive head then combines these bases as a restricted GAM-style predictor.