publication . Preprint . 2018

Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute

Wang, Tong;
Open Access English
  • Published: 12 Feb 2018
This work addresses the situation where a black-box model with good predictive performance is chosen over its interpretable competitors, and we show interpretability is still achievable in this case. Our solution is to find an interpretable substitute on a subset of data where the black-box model is overkill or nearly overkill while leaving the rest to the black-box. This transparency is obtained at minimal cost or no cost of the predictive performance. Under this framework, we develop a Hybrid Rule Sets (HyRS) model that uses decision rules to capture the subspace of data where the rules are as accurate or almost as accurate as the black-box provided. To train ...
free text keywords: Computer Science - Machine Learning, Statistics - Machine Learning
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