interpretml/interpret

[](https://mybinder.org/v2/gh/interpretml/interpret/main?labpath=docs%2Finterpret%2Fpython%2Fexamples%2Finterpretable-classification.ipynb) [](https://codecov.io/github/interpretml/interpret) InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: Python 3.10+ | Linux, Mac, Windows EBM is an interpretable model developed at Microsoft Research. It uses modern machine learning techniques like bagging, gradient boosting, and automatic interaction detection to breathe new life into traditional GAMs (Generalized Additive Models). This makes EBMs as accurate as state-of-the-art techniques like random forests and gradient boosted trees. However, unlike these blackbox models, EBMs produce exact explanations and are editable by domain experts. Notebook for reproducing table Let's fit an Explainable Boosting Machine Understand the model Understand individual predictions And if you have multiple model explanations, compare them If you need to keep your data…