论文标题
在增强树模型中的添加建模功能的塑造
SHAP for additively modeled features in a boosted trees model
论文作者
论文摘要
探索黑盒机器学习（ML）模型的重要技术称为Shap（Shapley添加说明）。 Shap值以公平的方式将预测分解为特征的贡献。我们将证明，对于具有添加性建模的一些或所有功能的增强树模型，此类特征的外形依赖图与其部分依赖图相对应，直到垂直移动。我们用XGBoost说明了结果。
An important technique to explore a black-box machine learning (ML) model is called SHAP (SHapley Additive exPlanation). SHAP values decompose predictions into contributions of the features in a fair way. We will show that for a boosted trees model with some or all features being additively modeled, the SHAP dependence plot of such a feature corresponds to its partial dependence plot up to a vertical shift. We illustrate the result with XGBoost.
