A general framework for constructing variable importance plots from various types of machine learning models in R. Aside from some standard model- specific variable importance measures, this package also provides model- agnostic approaches that can be applied to any supervised learning algorithm. These include 1) an efficient permutation-based variable importance measure, 2) variable importance based on Shapley values (Strumbelj and Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based approach described in Greenwell et al. (2018) <arXiv:1805.04755>. A variance-based method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).
Package details |
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Author | Brandon M. Greenwell [aut, cre] (<https://orcid.org/0000-0002-8120-0084>), Brad Boehmke [aut] (<https://orcid.org/0000-0002-3611-8516>) |
Maintainer | Brandon M. Greenwell <greenwell.brandon@gmail.com> |
License | GPL (>= 2) |
Version | 0.4.1 |
URL | https://github.com/koalaverse/vip/ https://koalaverse.github.io/vip/ |
Package repository | View on CRAN |
Installation |
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