shap

A game theoretic approach to explain the output of any machine learning model.

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

Fast SHAP value computation for interpreting tree-based models

Shapley Interactions and Shapley Values for Machine Learning

利用lightgbm做(learning to rank)排序学习，包括数据处理、模型训练、模型决策可视化、模型可解释性以及预测等。Use LightGBM to learn ranking, including data processing, model training, model decision visualization, model interpretability and prediction, etc.

A power-full Shapley feature selection method.

TimeSHAP explains Recurrent Neural Network predictions.

Automated Tool for Optimized Modelling

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

SHAP-based validation for linear and tree-based models. Applied to binary, multiclass and regression problems.

Explainable Machine Learning in Survival Analysis

SHAP Plots in R

A Julia package for interpretable machine learning with stochastic Shapley values

streamlit-shap provides a wrapper to display SHAP plots in Streamlit.

SurvSHAP(t): Time-dependent explanations of machine learning survival models

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model