iml

#计算机科学#Fit interpretable models. Explain blackbox machine learning.

#计算机科学#moDel Agnostic Language for Exploration and eXplanation

#计算机科学#Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

#计算机科学#H2O.ai Machine Learning Interpretability Resources

#学习与技能提升#📍 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 Classif...

#计算机科学#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

#计算机科学#Compute SHAP values for your tree-based models using the TreeSHAP algorithm

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

#计算机科学#Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

#计算机科学#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.

#计算机科学#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

#计算机科学#A Python package with explanation methods for extraction of feature interactions from predictive models