vivid: An R package for Variable Importance and Variable Interactions Displays for Machine Learning Models

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
R Journal Pub Date : 2023-11-01 DOI:10.32614/rj-2023-054
Alan Inglis, Andrew Parnell, Catherine Hurley
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引用次数: 0

Abstract

We present vivid, an R package for visualizing variable importance and variable interactions in machine learning models. The package provides heatmap and graph-based displays for viewing variable importance and interaction jointly, and partial dependence plots in both a matrix layout and an alternative layout emphasizing important variable subsets. With the intention of increasing machine learning models' interpretability and making the work applicable to a wider readership, we discuss the design choices behind our implementation by focusing on the package structure and providing an in-depth look at the package functions and key features. We also provide a practical illustration of the software in use on a data set.
vivid:一个R包,用于机器学习模型的可变重要性和可变交互显示
我们提出了一个生动的R包,用于可视化机器学习模型中的变量重要性和变量交互。该软件包提供了热图和基于图形的显示,用于共同查看变量的重要性和相互作用,以及在矩阵布局和强调重要变量子集的替代布局中的部分依赖图。为了提高机器学习模型的可解释性并使工作适用于更广泛的读者,我们通过关注包结构并深入研究包功能和关键特性来讨论实现背后的设计选择。我们还提供了在数据集上使用该软件的实际示例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
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