#Interpretable-machine-learning
Showing 25 of 25 repositories tagged #interpretable-machine-learning, ranked by stars
Fit interpretable models. Explain blackbox machine learning.
A curated list of awesome responsible machine learning resources.
Generate Diverse Counterfactual Explanations for any machine learning model.
moDel Agnostic Language for Exploration and eXplanation
[CONTRIBUTORS WELCOME] Generalized Additive Models in Python
OmniXAI: A Library for eXplainable AI
Shapley Interactions and Shapley Values for Machine Learning
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
H2O.ai Machine Learning Interpretability Resources
A Python library for Interpretable Machine Learning in Text Classification using the SS3 model, with easy-to-use visualization tools for Explainable AI :octocat:
📍 Interactive Studio for Explanatory Model Analysis
Concept Bottleneck Models, ICML 2020
🕵️♂️ Interpreting Convolutional Neural Network (CNN) Results.
PyTorch Explain: Interpretable Deep Learning in Python.
[EMNLP 2024] The official GitHub repo for the survey paper "Knowledge Conflicts for LLMs: A Survey"
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers
Model Agnostic Counterfactual Explanations
Implementation of the paper "Shapley Explanation Networks"
Information Bottlenecks for Attribution
This is an official implementation for PROMPT-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis (CVPR'25). Explore fine-grained trait distinctions between different specified species.
[ICLR 23 spotlight] An automatic and efficient tool to describe functionalities of individual neurons in DNNs
Quantified Sleep: Machine learning techniques for observational n-of-1 studies.
[EMNLP 25] An effective and interpretable weight-editing method for mitigating overly short reasoning in LLMs, and a mechanistic study uncovering how reasoning length is encoded in the model’s representation space.