Explainable Ai
Showing 21 of 21 projects
A unified Python library for explaining any machine learning model's predictions using Shapley values from game theory.
A PyTorch library providing state-of-the-art methods for generating visual explanations (Class Activation Maps) for computer vision models.
A Python library that explains predictions of any machine learning classifier using local interpretable model-agnostic explanations.
An open-source Python package for training interpretable glassbox models and explaining blackbox machine learning systems.
A Python library that makes machine learning models interpretable and transparent through user-friendly visualizations and a web application.
A Python library for explaining machine learning models using black-box, white-box, local, and global interpretation methods.
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 curated collection of research papers, books, courses, and Python libraries for explainable AI (XAI) and machine learning interpretability.
A model-agnostic toolkit for exploring and explaining the behavior of complex machine learning models in R and Python.
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 toolbox for visualizing feature influence on model predictions using partial dependence plots.
A scikit-learn compatible classifier that produces human-interpretable decision rules instead of black box models.
A Python library for interpretable text classification using the SS3 model, with built-in visualization tools for explainable AI.
A Python library for evaluating binary classifiers in machine learning ensembles using Shapley value computation and approximation methods.
A Python library for monitoring model and data drift over time, generating insightful HTML reports for AI governance.
A curated collection of papers, methods, critiques, and resources for Explainable AI (XAI) and Interpretable Machine Learning.
A Python machine learning and informatics suite for analyzing, mining, and modeling chemical and materials data.
A Python implementation of individual conditional expectation plots for visualizing machine learning model predictions.
A method for selecting interpretable feature subsets from complex models using mutual information optimization.
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