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We propose Partially Interpretable Estimators (PIE) which attribute aprediction to individual features via an interpretable model, while a(possibly) small part of the PIE prediction is attributed to the interaction offeatures via a black-box model, with the goal to boost the predictiveperformance while maintaining interpretability. As such, the interpretablemodel captures the main contributions of features, and the black-box modelattempts to complement the interpretable piece by capturing the "nuances" offeature interactions as a refinement. We design an iterative training algorithmto jointly train the two types of models. Experimental results show that PIE ishighly competitive to black-box models while outperforming interpretablebaselines. In addition, the understandability of PIE is comparable to simplelinear models as validated via a human evaluation.

Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning