Installation
About
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)
Citation | vip citation info |
github.com/koalaverse/vip/ | |
koalaverse.github.io/vip/ | |
Bug report | File report |
Key Metrics
Downloads
Yesterday | 344 +54% |
Last 7 days | 2.098 -25% |
Last 30 days | 9.222 +5% |
Last 90 days | 25.113 -4% |
Last 365 days | 103.164 -24% |
Depends
R | ≥ 4.1.0 |