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 an efficient permutation-based variable importance measure as well as novel approaches based on partial dependence plots (PDPs) and individual conditional expectation (ICE) curves which are described in Greenwell et al. (2018) <arXiv:1805.04755>. An experimental method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).
