Shap Open Source Projects

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

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

🔅 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

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.

SHAP Plots in R

Explainable Machine Learning in Survival Analysis

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

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