TimeSHAP explains Recurrent Neural Network predictions.
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Updated
Dec 21, 2023 - Jupyter Notebook
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
Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation (ACL 2020)
Experimental toolbox for quantum Shapley values.
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
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.
Using SHAP values to explain model features
API backend to deploy a machine learning model to the web
ML implementations in Multi-scale model for lignin biosynthesis in Populus Trichocarpa
An investigation on the use of shapley explanations for unsupervised anomaly-detection models
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