A game theoretic approach to explain the output of any machine learning model.
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Updated
Jun 30, 2024 - Jupyter Notebook
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.
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 python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
Validation (like Recursive Feature Elimination for SHAP) of (multiclass) classifiers & regressors and data used to develop them.
Fast SHAP value computation for interpreting tree-based models
TimeSHAP explains Recurrent Neural Network predictions.
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
A website that provides analytics on how different features contribute to your chances of getting into a university of your choice.
A power-full Shapley feature selection method.
Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Automated Tool for Optimized Modelling
Overview of different model interpretability libraries.
Local explanations with uncertainty 💐!
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Explainable Machine Learning in Survival Analysis
R package for SHAP plots
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