There are 1 repository under shapley-value topic.
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
SHAP Plots in R
This repo is the implementation of paper ''SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning''.
A Matlab Toolbox for Cooperative Game Theory
A pip library for calculating the Shapley Value for computing the marginal contribution of each client in a Federated Learning environment.
PyTorch reimplementation of computing Shapley values via Truncated Monte Carlo sampling from "What is your data worth? Equitable Valuation of Data" by Amirata Ghorbani and James Zou [ICML 2019]
This is the official source code for CVPR 2024 paper [WWW: A Unified Framework for Explaining What, Where and Why of Neural Networks by Interpretation of Neuron Concepts]
Game Theory model for multichannel marketing attribution
Codebase for "Greedy Shapley Client Selection for Communication-Efficient Federated Learning"
LINe: Out-of-Distribution Detection by Leveraging Important Neurons (CVPR 2023)
Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
A Mathematica Package for Cooperative Game Theory
Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation (ACL 2020)
Playground for testing Horizontal Federated Machine Learning systems using the Shapley Value for profit allocation
HERALD: An Annotation Efficient Method to Train User Engagement Predictors in Dialogs (ACL 2021)
Applying GradCAM method with 3 kinds of CNN-based model for NLP classification task on french dataset.
Explaining Trees (LightGBM) with FastTreeShap (Shapley) and What if tool
Applying ML interpretation methods on the pet-finder Kaggle challenge
Predicting NBA game outcomes using schedule related information. This is an example of supervised learning where a xgboost model was trained with 20 seasons worth of NBA games and uses SHAP values for model explainability.
Implementation of the algorithm described in the paper "An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data"
This repository consists the supplemental materials of the paper "Decomposition of Expected Goal Models: Aggregated SHAP Values for Analyzing Scoring Potential of Player/Team".