Interpretability
Showing 19 of 19 projects
A unified Python library for explaining any machine learning model's predictions using Shapley values from game theory.
A curated list of awesome open-source libraries for deploying, monitoring, versioning, and scaling production machine learning systems.
A curated list of awesome open-source libraries for deploying, monitoring, versioning, and scaling production machine learning systems.
A PyTorch library providing state-of-the-art methods for generating visual explanations (Class Activation Maps) for computer vision models.
An open-source Python package for training interpretable glassbox models and explaining blackbox machine learning systems.
A collection of infrastructure and tools for research in neural network interpretability and visualization.
A curated list of practical resources for responsible machine learning, covering interpretability, governance, safety, and ethics.
A Python library that makes machine learning models interpretable and transparent through user-friendly visualizations and a web application.
A Python library for machine learning on graphs and networks, offering state-of-the-art algorithms for tasks like node classification and link prediction.
A Python library for explaining machine learning models using black-box, white-box, local, and global interpretation methods.
A curated collection of research papers on decision, classification, and regression trees with implementations from top ML conferences.
A JAX research toolkit for building, editing, and visualizing neural networks as legible, functional pytree data structures.
A curated collection of research papers, books, courses, and Python libraries for explainable AI (XAI) and machine learning interpretability.
A Python package for concise, transparent, and accurate predictive modeling with sklearn-compatible interpretable models.
A model-agnostic toolkit for exploring and explaining the behavior of complex machine learning models in R and Python.
A TensorFlow library for training, serving, and interpreting decision forest models like Random Forests and Gradient Boosted Trees.
A comprehensive PhD dissertation providing an in-depth theoretical and practical analysis of random forests, from algorithmic foundations to interpretability.
A curated list of resources for understanding, measuring, and mitigating fairness issues in artificial intelligence and machine learning systems.
A Python implementation of individual conditional expectation plots for visualizing machine learning model predictions.
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