shapley-value
There are 82 repositories under shapley-value topic.
benedekrozemberczki/shapley
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
dominance-analysis/dominance-analysis
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.
AstraZeneca/awesome-shapley-value
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
aai-institute/pyDVL
pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation
nredell/ShapML.jl
A Julia package for interpretable machine learning with stochastic Shapley values
ModelOriented/treeshap
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
ModelOriented/shapviz
SHAP Plots in R
nredell/shapFlex
An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
hsvgbkhgbv/shapley-q-learning
This repo is the implementation of paper ''SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning''.
himeinhardt/MatTuGames
A Matlab Toolbox for Cooperative Game Theory
easeml/datascope
Measuring data importance over ML pipelines using the Shapley value.
akassharjun/ShapleyValueFL
A pip library for calculating the Shapley Value for computing the marginal contribution of each client in a Federated Learning environment.
ajsanjoaquin/Shapley_Valuation
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]
BUAA-BDA/FedShapley
Profit Allocation for Federated Learning
kuffmode/msa
Hopefully, a compact and general-purpose Python package for Multiperturbation Shapley value Analysis (MSA).
ailab-kyunghee/WWW
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]
jrkinley-zz/game-theory-attribution
Game Theory model for multichannel marketing attribution
pringlesinghal/GreedyFed
Codebase for "Greedy Shapley Client Selection for Communication-Efficient Federated Learning"
YongHyun-Ahn/LINe-Out-of-Distribution-Detection-by-Leveraging-Important-Neurons
LINe: Out-of-Distribution Detection by Leveraging Important Neurons (CVPR 2023)
haghish/shapley
Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
hi-paris/XPER
A methodology designed to measure the contribution of the features to the predictive performance of any econometric or machine learning model.
himeinhardt/TuGames
A Mathematica Package for Cooperative Game Theory
Weixin-Liang/dialog_evaluation_CMADE
Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation (ACL 2020)
tsurubee/shappack
Interpretable machine learning based on Shapley values
clickade/federated-shapley-playground
Playground for testing Horizontal Federated Machine Learning systems using the Shapley Value for profit allocation
Weixin-Liang/HERALD
HERALD: An Annotation Efficient Method to Train User Engagement Predictors in Dialogs (ACL 2021)
ENSAE-CKW/nlp_understanding
Applying GradCAM method with 3 kinds of CNN-based model for NLP classification task on french dataset.
zabir-nabil/What-If-Explainability
Explaining Trees (LightGBM) with FastTreeShap (Shapley) and What if tool
fabianmax/pet-finder
Applying ML interpretation methods on the pet-finder Kaggle challenge
jluchman/domin
Dominance Analysis: Stata Implementation
jluchman/domir
Tools to Support Relative Importance Analysis
LLeiSong/itsdm
Purely presence-only species distribution modeling with isolation forest and its variations such as Extended isolation forest and SCiForest.
josedv82/NBA_Schedule_XGBoost_Classifier
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.
LightnessOfBeing/ImpreciseSHAP
Implementation of the algorithm described in the paper "An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data"
mayer79/permshap
Permutation SHAP
mcavs/Decomposition-of-Expected-Goal-Models
This repository consists the supplemental materials of the paper "Decomposition of Expected Goal Models: Aggregated SHAP Values for Analyzing Scoring Potential of Player/Team".