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Interpretable machine learning has become a strong competitor for traditionalblack-box models. However, the possible loss of the predictive performance forgaining interpretability is often inevitable, putting practitioners in adilemma of choosing between high accuracy (black-box models) andinterpretability (interpretable models). In this work, we propose a novelframework for building a Hybrid Predictive Model (HPM) that integrates aninterpretable model with any black-box model to combine their strengths. Theinterpretable model substitutes the black-box model on a subset of data wherethe black-box is overkill or nearly overkill, gaining transparency at no or lowcost of the predictive accuracy. We design a principled objective function thatconsiders predictive accuracy, model interpretability, and model transparency(defined as the percentage of data processed by the interpretable substitute.)Under this framework, we propose two hybrid models, one substituting withassociation rules and the other with linear models, and we design customizedtraining algorithms for both models. We test the hybrid models on structureddata and text data where interpretable models collaborate with variousstate-of-the-art black-box models. Results show that hybrid models obtain anefficient trade-off between transparency and predictive performance,characterized by our proposed efficient frontiers.

Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model