A game theoretic approach to explain the output of any machine learning model.
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Updated
Oct 13, 2024 - Jupyter Notebook
A game theoretic approach to explain the output of any machine learning model.
Explaining the output of machine learning models with more accurately estimated Shapley values
For calculating global feature importance using Shapley values.
Analytical computation of rolling and expanding Shapley values for time-series data.
Analysing Time series and spatiotemporal data
A Julia package for interpretable machine learning with stochastic Shapley values
Fast approximate Shapley values in R
Résumé de mes projets de Machine Learning
Personal website of Frank Huettner
This repository contains the code for machine learning models designed to predict the outcomes of horse races, with SHAP (SHapley Additive exPlanations) interpretation incorporated for enhanced model interpretability.
Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)
Examines fairness metrics for models including gender stereotyping versus group differences due to appropriate predictors. Also explores feature bias mitigation
Flask app that predicts the risk of heart disease based on a GBT ML model, and shows the confidence in the prediction as well as the factors behind the prediction (explainability).
A solution concept in cooperative game theory
This is an official repository for "2D-Shapley: A Framework for Fragmented Data Valuation" (ICML2023).
Trained a classifier by using labeled data and oversampling and undersampling techniques to predict if a borrower will default on a loan. The model is intended to be used as a reference tool to help investors make informed decisions about lending to potential borrowers based on their ability to repay. The purpose is to lower risk & maximize profit.
Counterfactual SHAP: a framework for counterfactual feature importance
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