TimeSHAP explains Recurrent Neural Network predictions.

Paper collection of federated learning. Conferences and Journals Collection for Federated Learning from 2019 to 2021, Accepted Papers, Hot topics and good research groups. Paper summary

Fast approximate Shapley values in R

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

Counterfactual SHAP: a framework for counterfactual feature importance

Counterfactual Shapley Additive Explanation: Experiments

Slides for the "Interpretable SDM with Julia" workshop

Shapley-based decomposition to anatomize the of out-of-sample accuracy of time-series forecasting models

A radiomic interpretation tool based on Shapley values

[IJCAI 2024] Redefining Contributions: Shapley-Driven Federated Learning

Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles

Experimental toolbox for quantum Shapley values.

Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation (ACL 2020)

This repository is the official implementation of Explainable Prediction of Acute Myocardial Infarction using Machine Learning and Shapley Values published in IEEE Access in November 2020.

A Proxy-Based Algorithm for Explaining Survival Models with SHAP

Two Group Recommendation Approaches based on the Contribution of the Users and Pairwise Preferences

Why do employees leave? This project first compares the predictive performance of three different models, then uses the best model to help reveal the top contributing factors.

Heart disease prediction by exploring different models, and feature importance visualization

Python/Jupyter Notebook to my Bachelor-Thesis in Computer Science. Explains contributions of features that are not part of a Machine Learning model by using Transfer Learning and Shapley Values/SHAP.

Group Recommendation Systems with Diversity-based Clustering and Game Theory