Shap Collection

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

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

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

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“Shapley Interactions and Shapley Values for Machine Learning”

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

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“Fast SHAP value computation for interpreting tree-based models”

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“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.”

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“A power-full Shapley feature selection method. ”

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“TimeSHAP explains Recurrent Neural Network predictions.”

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“Automated Tool for Optimized Modelling”

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

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

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“SHAP Plots in R”

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“Explainable Machine Learning in Survival Analysis”

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

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

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“A Julia package for interpretable machine learning with stochastic Shapley values”

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