Abstract: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. Rather than reducing triggered rules to compact count summaries, FlagGAM retains a sparse rule-basis matrix that supports mixed-type classification and regression, feature-specific weighting, and optional flexible prediction heads. Across tabular benchmarks, default FlagGAM remains close to EBM in transparent additive mode, improves substantially over ridge regression on mixed-type regression, and shows smaller AUROC degradation than common baselines under missing and noisy perturbations. Flexible heads further improve accuracy and approach strong tree-based baselines, with the caveat that the resulting model should be interpreted as a rule-basis representation followed by a nonlinear predictor rather than as a fully additive GAM. Overall, FlagGAM provides a practical middle ground for tabular settings that require competitive accuracy, communicable rules, and robustness to imperfect inputs.
From: Zijie Zhao [view email]
[v1]
Fri, 29 May 2026 11:56:33 UTC (690 KB)
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