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We introduce a family of interpretable machine learning models, with twobroad additions: Linearised Additive Models (LAMs) which replace the ubiquitouslogistic link function in General Additive Models (GAMs); and SubscaleHedge, anexpert advice algorithm for combining base models trained on subsets offeatures called subscales. LAMs can augment any additive binary classificationmodel equipped with a sigmoid link function. Moreover, they afford directglobal and local attributions of additive components to the model output inprobability space. We argue that LAMs and SubscaleHedge improve theinterpretability of their base algorithms. Using rigorous null-hypothesissignificance testing on a broad suite of financial modelling data, we show thatour algorithms do not suffer from large performance penalties in terms ofROC-AUC and calibration.

Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning