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

📍 Interactive Studio for Explanatory Model Analysis

💡 Adversarial attacks on explanations and how to defend them

[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"

Model Agnostics breakDown plots

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

A Julia package for interpretable machine learning with stochastic Shapley values

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

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.

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

Data generator for Arena - interactive XAI dashboard

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