Model Interpretability

shapJupyter Notebook

A unified Python library for explaining any machine learning model's predictions using Shapley values from game theory.

pytorch-grad-camPython

A PyTorch library providing state-of-the-art methods for generating visual explanations (Class Activation Maps) for computer vision models.

LimeJavaScript

A Python library that explains predictions of any machine learning classifier using local interpretable model-agnostic explanations.

shapashJupyter Notebook

A Python library that makes machine learning models interpretable and transparent through user-friendly visualizations and a web application.

AlibiPython

A Python library for explaining machine learning models using black-box, white-box, local, and global interpretation methods.

PenzaiPython

A JAX research toolkit for building, editing, and visualizing neural networks as legible, functional pytree data structures.

AI Explainability 360Python

An open-source Python toolkit providing a comprehensive collection of algorithms for interpreting and explaining machine learning models and datasets.