  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.
