iml

Fit interpretable models. Explain blackbox machine learning.

moDel Agnostic Language for Exploration and eXplanation

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

💡 Adversarial attacks on explanations and how to defend them

📍 Interactive Studio for Explanatory Model Analysis

[NeurIPS'24 Spotlight] A comprehensive benchmark & codebase for Image manipulation detection/localization.

Effector - a Python package for global and regional effect methods

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

An interactive framework to visualize and analyze your AutoML process in real-time.

Model Agnostics breakDown plots

A Julia package for interpretable machine learning with stochastic Shapley values

Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

Unofficial implementation of MVSS-Net (ICCV 2021) with Pytorch including training code.

iLang 专家与技能的 GitHub 安装源，海外版 WorkBuddy 可直接装：公众号爆文结构专家 v2.0（素材进稿子出，12 项自查，内置去 AI 味）· iLang experts & skills for WorkBuddy, installable from GitHub

Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN

A Python package with explanation methods for extraction of feature interactions from predictive models