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arXiv · 1808.00033

Techniques for Interpretable Machine Learning

Mengnan Du·Ninghao Liu·Xia Hu
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Abstract

Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision. Although many approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. We provide a survey covering existing techniques to increase the interpretability of machine learning models. We also discuss crucial issues that the community should consider in future work such as designing user-friendly explanations and developing comprehensive evaluation metrics to further push forward the area of interpretable machine learning.

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Mengnan Du, Ninghao Liu, Xia Hu. 2018-07-31. Techniques for Interpretable Machine Learning. https://arxiv.org/abs/1808.00033

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