Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute

12 Feb 2018 Tong Wang

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... (read more)

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