ExplaineR: 用于解释机器学习模型的 R 软件包。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Bioinformatics advances Pub Date : 2024-03-26 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae049
Ramtin Zargari Marandi
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引用次数: 0

摘要

摘要:SHapley Additive exPlanations(SHAP)是一种广泛使用的模型解释方法。然而,由于缺乏专用软件工具,它的全部潜力往往仍未得到开发。为此,本文介绍了一个基于聚类功能的 R 软件包 ExplaineR,以方便解释二元分类和回归模型的 SHAP 分析。此外,它还在可视化方面提供了用户互动元素,用于评估模型性能、公平性分析、决策曲线分析和各种 SHAP 图。它有助于对模型进行深入的预测后分析,使用户能够在 SHAP 图中找出潜在的重要模式,并随后通过 SHAP 聚类追溯到实例。这一功能对于识别临床队列中的患者亚群尤为重要,从而增强了其作为强大的剖析工具的作用。ExplaineR 使用户能够生成有关机器学习结果的综合报告,确保对模型性能和解释进行一致而全面的记录:ExplaineR 1.0.0 可在 GitHub (https://persimune.github.io/explainer/) 和 CRAN (https://cran.r-project.org/web/packages/explainer/index.html) 上获取。
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ExplaineR: an R package to explain machine learning models.

Summary: SHapley Additive exPlanations (SHAP) is a widely used method for model interpretation. However, its full potential often remains untapped due to the absence of dedicated software tools. In response, ExplaineR, an R package to facilitate interpretation of binary classification and regression models based on clustering functionality for SHAP analysis is introduced here. It additionally offers user-interactive elements in visualizations for evaluating model performance, fairness analysis, decision-curve analysis, and a diverse range of SHAP plots. It facilitates in-depth post-prediction analysis of models, enabling users to pinpoint potentially significant patterns in SHAP plots and subsequently trace them back to instances through SHAP clustering. This functionality is particularly valuable for identifying patient subgroups in clinical cohorts, thus enhancing its role as a robust profiling tool. ExplaineR empowers users to generate comprehensive reports on machine learning outcomes, ensuring consistent and thorough documentation of model performance and interpretations.

Availability and implementation: ExplaineR 1.0.0 is available on GitHub (https://persimune.github.io/explainer/) and CRAN (https://cran.r-project.org/web/packages/explainer/index.html).

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