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

• URL: http://arxiv.org/abs/2105.02410v1
• Date: Thu, 6 May 2021 03:06:34 GMT
• Title: Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning
• Authors: Tong Wang, Jingyi Yang, Yunyi Li, Boxiang Wang
• Abstract summary: We propose Partially Interpretable Estimators (PIE) which attribute a prediction to individual features via an interpretable model. We design an iterative training algorithm to jointly train the two types of models. Experimental results show that PIE is highly competitive to black-box models while outperforming interpretable baselines.
• Score: 5.479705009242287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Partially Interpretable Estimators (PIE) which attribute a prediction to individual features via an interpretable model, while a (possibly) small part of the PIE prediction is attributed to the interaction of features via a black-box model, with the goal to boost the predictive performance while maintaining interpretability. As such, the interpretable model captures the main contributions of features, and the black-box model attempts to complement the interpretable piece by capturing the "nuances" of feature interactions as a refinement. We design an iterative training algorithm to jointly train the two types of models. Experimental results show that PIE is highly competitive to black-box models while outperforming interpretable baselines. In addition, the understandability of PIE is comparable to simple linear models as validated via a human evaluation.

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