A game theoretic approach to explain the output of any machine learning model.
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Updated
May 16, 2024 - Jupyter Notebook
A game theoretic approach to explain the output of any machine learning model.
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
Multi-Touch Attribution
For calculating global feature importance using Shapley values.
Explaining the output of machine learning models with more accurately estimated Shapley values
Amazon SageMaker Solution for explaining credit decisions.
Fast approximate Shapley values in R
Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)
For calculating Shapley values via linear regression.
ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based models.
A lightweight implementation of removal-based explanations for ML models.
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
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
streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
Counterfactual Shapley Additive Explanation: Experiments
A Julia package for interpretable machine learning with stochastic Shapley values
Jupyter Notebook Templates for quick prototyping of machine learning solutions
Shapley Values with H2O AutoML Example (ML Interpretability)
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