论文标题
Shapley可变的重要性云用于机器学习模型
Shapley variable importance cloud for machine learning models
论文作者
论文摘要
当前可解释的机器学习中的实践通常集中于通过使用Shapley添加说明（SHAP）方法来解释从数据训练的最终模型。最近开发的Shapley变量重要性云（Shapleyvic）将当前的实践扩展到一组“几乎最佳模型”，以提供全面且健壮的可变重要性评估，并具有估计的不确定性间隔，以对预测的可变贡献有更全面的理解。 Shapleyvic最初是针对传统回归模型的应用开发的，Shapleyvic推断的好处在现实生活预测任务中已通过Logistic Remission模型进行了证明。但是，作为一种模型无关的方法，Shapleyvic的应用不限于这种情况。在这项工作中，我们扩展了机器学习模型的Shapleyvic实现，以启用更广泛的应用程序，并将其作为当前Shap分析的有用补充，以启用这些Black-Box模型的更值得信赖的应用程序。
Current practice in interpretable machine learning often focuses on explaining the final model trained from data, e.g., by using the Shapley additive explanations (SHAP) method. The recently developed Shapley variable importance cloud (ShapleyVIC) extends the current practice to a group of "nearly optimal models" to provide comprehensive and robust variable importance assessments, with estimated uncertainty intervals for a more complete understanding of variable contributions to predictions. ShapleyVIC was initially developed for applications with traditional regression models, and the benefits of ShapleyVIC inference have been demonstrated in real-life prediction tasks using the logistic regression model. However, as a model-agnostic approach, ShapleyVIC application is not limited to such scenarios. In this work, we extend ShapleyVIC implementation for machine learning models to enable wider applications, and propose it as a useful complement to the current SHAP analysis to enable more trustworthy applications of these black-box models.
