What’s the Price of Monotonicity? A Multi‑Dataset Benchmark of Monotone‑Constrained Gradient Boosting for Credit PD
创建于 更新于
摘要
This paper defines a Price of Monotonicity (PoM) metric and benchmarks monotone‑constrained vs unconstrained gradient boosting (XGBoost, LightGBM, CatBoost) across five public credit PD datasets, showing that PoM in AUC is typically near zero on large portfolios (often <0.2%) but can reach ~2–2.9% on smaller, high-coverage datasets, while wrong-sign constraints inflict substantially larger costs; paired bootstrap is used to quantify uncertainty [page::0][page::40]
