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arXiv · 1909.09223

InterpretML: A Unified Framework for Machine Learning Interpretability

Harsha Nori·Samuel Jenkins·Paul Koch·Rich Caruana
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Abstract

InterpretML is an open-source Python package which exposes machine learning interpretability algorithms to practitioners and researchers. InterpretML exposes two types of interpretability - glassbox models, which are machine learning models designed for interpretability (ex: linear models, rule lists, generalized additive models), and blackbox explainability techniques for explaining existing systems (ex: Partial Dependence, LIME). The package enables practitioners to easily compare interpretability algorithms by exposing multiple methods under a unified API, and by having a built-in, extensible visualization platform. InterpretML also includes the first implementation of the Explainable Boosting Machine, a powerful, interpretable, glassbox model that can be as accurate as many blackbox models. The MIT licensed source code can be downloaded from github.com/microsoft/interpret.

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Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana. 2019-09-19. InterpretML: A Unified Framework for Machine Learning Interpretability. https://arxiv.org/abs/1909.09223

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