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).

Last 30 days

This package has been downloaded 9,290 times in the last 30 days. Impressive! The kind of number that makes colleagues ask, 'How did you do it?' The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 522 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 89,311 times in the last 365 days. An impressive feat! Enough downloads to make even seasoned academics take note. The day with the most downloads was Sep 24, 2024 with 542 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.