interpretable-machine-learning

Fit interpretable models. Explain blackbox machine learning.

A curated list of awesome responsible machine learning resources.

moDel Agnostic Language for Exploration and eXplanation

Generate Diverse Counterfactual Explanations for any machine learning model.

PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics

OmniXAI: A Library for eXplainable AI

[HELP REQUESTED] Generalized Additive Models in Python

Interesting resources related to XAI (Explainable Artificial Intelligence)

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

Shapley Interactions and Shapley Values for Machine Learning

H2O.ai Machine Learning Interpretability Resources

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu

A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI <img alt="octocat" src="https://github.githubassets.com/images/icons/emoji/octocat.png?v8" />)

📍 Interactive Studio for Explanatory Model Analysis

💡 Adversarial attacks on explanations and how to defend them

PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

Concept Bottleneck Models, ICML 2020

🕵️‍♂️ Interpreting Convolutional Neural Network (CNN) Results.

🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease