Conference proceeding
Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 97, Vol.97
Proceedings of Machine Learning Research
01/01/2019
Abstract
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 a HyRS, we devise an efficient search algorithm that iteratively finds the optimal model and exploits theoretically grounded strategies to reduce computation. Our framework is agnostic to the black-box during training Experiments on structured and text data show that HyRS obtains an effective trade-off between transparency and interpretability.
Details
- Title: Subtitle
- Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute
- Creators
- Tong Wang - Univ Iowa, Dept Business Analyt, Iowa City, IA 52242 USA
- Contributors
- K Chaudhuri (Editor)R Salakhutdinov (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 97, Vol.97
- Publisher
- JMLR-JOURNAL MACHINE LEARNING RESEARCH
- Series
- Proceedings of Machine Learning Research
- ISSN
- 2640-3498
- Number of pages
- 10
- Language
- English
- Date published
- 01/01/2019
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380384902771
Metrics
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