#计算机科学#A game theoretic approach to explain the output of any machine learning model.
#计算机科学#🔅 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.
#计算机科学#Shapley Interactions and Shapley Values for Machine Learning
#计算机科学#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 pre...
#计算机科学#A power-full Shapley feature selection method.
#计算机科学#Automated Tool for Optimized Modelling
#计算机科学#Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
#计算机科学#Explainable Machine Learning in Survival Analysis
A Julia package for interpretable machine learning with stochastic Shapley values
#学习与技能提升#SurvSHAP(t): Time-dependent explanations of machine learning survival models
#计算机科学#Compute SHAP values for your tree-based models using the TreeSHAP algorithm
#计算机科学#streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
#计算机科学#Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
#计算机科学#An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model