Model Interpretability
Showing 15 of 15 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.
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 JAX research toolkit for building, editing, and visualizing neural networks as legible, functional pytree data structures.
An open-source Python toolkit providing a comprehensive collection of algorithms for interpreting and explaining machine learning models and datasets.
A model-agnostic toolkit for exploring and explaining the behavior of complex machine learning models in R and Python.
An interactive visual interface for exploring and debugging black-box machine learning models without writing code.
A Python toolbox for visualizing feature influence on model predictions using partial dependence plots.
A TensorFlow library for training, serving, and interpreting decision forest models like Random Forests and Gradient Boosted Trees.
A PyTorch adaptation of Lucid for visualizing and interpreting neural networks through feature visualization.
A curated collection of papers, methods, critiques, and resources for Explainable AI (XAI) and Interpretable Machine Learning.
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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