Shypd.aiThe largest open index of AI toolsInterpretable Machine Learning with Python is an Open Source & Models tool that provides examples for training, explaining, and debugging ML models. It focuses on accuracy, discrimination, and security in machine learning practices.
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Interpretable Machine Learning with Python offers a collection of Jupyter notebooks demonstrating techniques for building responsible and transparent machine learning models. It covers methods for training interpretable ML models, explaining their predictions, and debugging them for issues related to accuracy, discrimination, and security. The notebooks introduce concepts such as Monotonic XGBoost, partial dependence, individual conditional expectation plots, Shapley explanations, decision tree surrogates, disparate impact analysis (DIA), LIME, and sensitivity/residual analysis. This resource is ideal for data scientists and analysts who need to understand, validate, and communicate their ML models, especially in regulated environments or when addressing concerns about fairness and trustworthiness.
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