[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120707-en":3,"doc-seo-120707-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120707,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","vivid - An R package for Variable Importance and Variable Interactions Displays for Machine Learning Models","vivid is an R package designed to visualize variable importance and variable interactions in machine learning models. It supports heatmap and graph-based displays to examine importance and interactions jointly, and provides partial dependence plots in a matrix layout as well as an alternative layout that emphasizes important variable subsets. The work focuses on interpretability, explains the implementation design choices through the package structure and functions, and includes a practical illustration on a real dataset.","arXiv :2210 . 11391v2 [ stat .CO] 23 Jun 2023  \nvivid: An R package for Variable Importance and Variable Interactions Displays for Machine Learning Models  \nby Alan Inglis, Andrew Parnell, and Catherine Hurley  \nAbstract 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 a 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.  \nIntroduction  \nOur motivation behind the creation of the vivid package is to investigate machine learning models ina way that is simple to understand while also offering helpful insights into how variables affect the fit. We do this through the use of heatmaps, network graphs, and both a generalized pairs plot style partial dependence plot (PDP) (Friedman 2000) and a space saving PDP based on key variable subsets. While the techniques and fundamental goals of these visualizations have been discussed in Inglis, Parnell, and Hurley (2022a), we focus here on the implementation details of the package by providing a complete listing of the functions and arguments included in the vivid package with further examples indicating advanced usage beyond that previously shown. In this work we examine the decisions made when designing the package and provide an in-depth look at the package functions and features with the intention of making the work applicable to a larger readership. This article outlines the general architectural principles implemented in vivid, such as the data structures we use and data formatting, function design, filtering techniques, and more. We illustrate each function by way of a practical example. Our package is available on the Comprehensive R Archive Network at [https://cran. r](https://cran. r)[project.org/web/packages/vivid](project.org/web/packages/vivid or on GitHub at)[ or on GitHub at](project.org/web/packages/vivid or on GitHub at) [https://github.com/AlanInglis/vivid](https://github.com/AlanInglis/vivid).  \nIn recent years machine learning (ML) algorithms have emerged as a valuable tool for both industry and science. However, due to the black-box nature of many of these algorithms it can be challenging to communicate the reasoning behind the algorithm's decision-making processes. With the need for transparency in ML growing it is important to gain understanding and clarity about how these algorithms are making predictions (Antunes et al. 2018; Felzmann et al. 2019) . Many R packages are now available that aid in creating interpretable machine learning (IML) models such as iml (Molnar, Bischl, and Casalicchio 2018), DALEX (Biecek 2018), and lime (Hvitfeldt, Pedersen, and Benesty 2022) . For a comprehensive review of IML, see Molnar (2022) and Biecek and Burzykowski (2021) .  \nHow we choose to visualize aspects of the model output is of vital importance in how a researcher can interpret and communicate their findings. Consequently, model summaries such as variable importance and variable interactions (VImp and VInt; together we term these VIVI) are frequently used in various fields to comprehend and explain the hidden structure in an ML fit. In ecology they are employed to determine the causes of ecological phenomena (e.g. Murray and Conner 2009); in meteorology VImp measures and partial dependence plots are used to examine air quality (e.g. Grange et al. 2018); in bioinformatics, understanding gene-environment interactions have made these measures an important tool for genomic a","cbCaiiAwKohnaCFH","https://ap.wps.com/l/cbCaiiAwKohnaCFH","pdf",810051,1,18,"English","en",105,"# Abstract\n# Introduction\n## Motivation and problem of interpretability\n## Visualizing variable importance and interactions (VIVI)\n## Taxonomy of VIVI measures and visualization types\n## Survey of R packages (Table 1)","[{\"question\":\"What does the vivid R package help with in machine learning models?\",\"answer\":\"It visualizes variable importance and variable interactions to improve understanding of how predictors affect model behavior.\"},{\"question\":\"Which kinds of visualizations does vivid provide?\",\"answer\":\"It offers heatmaps, graph-based displays, and partial dependence plots, including layouts that highlight important variable subsets.\"},{\"question\":\"How does vivid approach interpretability and explainability?\",\"answer\":\"It targets increased machine learning interpretability by presenting variable importance and interaction information jointly and in structured display formats, and it documents implementation design choices and package functions.\"}]","vivid - 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