We introduce a family of interpretable machine learning models, with two broad additions: Linearised Additive Models (LAMs) which replace the ubiquitous logistic link function in General Additive Models (GAMs); and SubscaleHedge, an expert advice algorithm for combining base models trained on subsets of features called subscales. LAMs can augment any additive binary classification model equipped with a sigmoid link function. Moreover, they afford direct global and local attributions of additive components to the model output in probability space. We argue that LAMs and SubscaleHedge improve the interpretability of their base algorithms. Using rigorous null-hypothesis significance testing on a broad suite of financial modelling data, we show that our algorithms do not suffer from large performance penalties in terms of ROC-AUC and calibration.

本研究提出了一系列可解释的机器学习模型，包括使用 Linearised Additive Models 和 SubscaleHedge 算法的 GAMs，并通过对广泛的金融建模数据进行严格的假设检验，表明这些算法在 ROC-AUC 和校准方面没有明显的性能下降。

重新思考对数几率：线性概率建模与可解释的机器学习中的专家建议