vip

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) doi:10.48550/arXiv.1805.04755. A variance-based method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).

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