vivid: An R package for Variable Importance and Variable Interactions Displays for Machine Learning Models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
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| _version_ | 1866909346936389632 |
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| author | Inglis, Alan Parnell, Andrew Hurley, Catherine |
| author_facet | Inglis, Alan Parnell, Andrew Hurley, Catherine |
| contents | We present vivid, an R package for visualizing variable importance and variable interactions in machine learning models. The package provides a range of displays including 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_11391 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | vivid: An R package for Variable Importance and Variable Interactions Displays for Machine Learning Models Inglis, Alan Parnell, Andrew Hurley, Catherine Computation We present vivid, an R package for visualizing variable importance and variable interactions in machine learning models. The package provides a range of displays including 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. |
| title | vivid: An R package for Variable Importance and Variable Interactions Displays for Machine Learning Models |
| topic | Computation |
| url | https://arxiv.org/abs/2210.11391 |