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 * [yellowbrick](https://github.com/DistrictDataLabs/yellowbrick) - Visual analysis and diagnostic tools to facilitate machine learning model selection. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [scikit-plot](https://github.com/reiinakano/scikit-plot) - An intuitive library to add plotting functionality to scikit-learn objects. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [shap](https://github.com/slundberg/shap) - A unified approach to explain the output of any machine learning model. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [InterpretML](https://github.com/interpretml/interpret) - InterpretML implements the Explainable Boosting Machine (EBM), a modern, fully interpretable machine learning model based on Generalized Additive Models (GAMs). This open-source package also provides visualization tools for EBMs, other glass-box models, and black-box explanations. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [ELI5](https://github.com/TeamHG-Memex/eli5) - A library for debugging/inspecting machine learning classifiers and explaining their predictions.
 * [Lime](https://github.com/marcotcr/lime) - Explaining the predictions of any machine learning classifier. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [FairML](https://github.com/adebayoj/fairml) - FairML is a python toolbox auditing the machine learning models for bias. <img height="20" src="img/sklearn_big.png" alt="sklearn">