{"id":676311,"date":"2020-07-17T13:51:25","date_gmt":"2020-07-17T20:51:25","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=676311"},"modified":"2023-03-21T16:35:32","modified_gmt":"2023-03-21T23:35:32","slug":"interpretml-a-unified-framework-for-machine-learning-interpretability","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/interpretml-a-unified-framework-for-machine-learning-interpretability\/","title":{"rendered":"InterpretML: A Unified Framework for Machine Learning Interpretability"},"content":{"rendered":"

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 this http URL.<\/p>\n","protected":false},"excerpt":{"rendered":"

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 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