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A Python library that makes machine learning models interpretable and transparent through user-friendly visualizations and a web application.
A curated collection of research papers and software for explainable graph machine learning and reasoning.
An open-source Python toolkit providing a comprehensive collection of algorithms for interpreting and explaining machine learning models and datasets.
A Python toolbox for explainable AI, providing tools for data analysis, model evaluation, and bias mitigation in machine learning.
A Python library for building Generalized Additive Models (GAMs) with a scikit-learn-like API, emphasizing interpretability and performance.
A Python library for evaluating binary classifiers in machine learning ensembles using Shapley value computation and approximation methods.
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