How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training inherently transparent models and applying post-hoc explanation techniques to make machine learning predictions human-understandable. The framework distinguishes itself by integrating differential privacy into the training of interpretable models to prevent sensitive data from leaking through explanations. It also features a visualization tool for rendering interactive decision paths and model behavior. The library covers model explainability through feature importance calcu
Source code/webpage/demos for the What-If Tool
Captum is an open-source library for explaining model predictions by attributing them to input features, neurons, and layers using gradient-based and perturbation-based methods. It provides a modular framework for implementing, evaluating, and combining a range of explanation techniques, including gradient-based attribution, perturbation-based analysis, game-theoretic Shapley value approximation, and surrogate model explanations, with support for parallelization and noise stabilization. The library distinguishes itself through its breadth of attribution methods and its support for advanced in
Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
The main features of understandable-machine-intelligence-lab/quantus are: Explainability and Fairness, Explainable AI Libraries.
Projects with overlapping indexed features include: pair-code/what-if-tool — Source code/webpage/demos for the What-If Tool. seldonio/alibi — Algorithms for explaining machine learning models. aerdem4/lofo-importance — Leave One Feature Out Importance. interpretml/interpret — Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training… pytorch/captum — Captum is an open-source library for explaining model predictions by attributing them to input features, neurons, and… trusted-ai/aif360 — A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and…
