vip: vi_shap – R documentation

Pricing

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

Get Started for Free

Documentation

vip

vi_shap

SHAP-based variable importance

Description

Compute SHAP-based VI scores for the predictors in a model. See details below.

Usage

vi_shap(object, ...)

## Default S3 method:
vi_shap(object, feature_names = NULL, train = NULL, ...)

Arguments

`object`	A fitted model object (e.g., a `"randomForest"` object).
`...`	Additional optional arguments to be passed on to `explain`.
`feature_names`	Character string giving the names of the predictor variables (i.e., features) of interest. If `NULL` (the default) then the internal 'get_feature_names()' function will be called to try and extract them automatically. It is good practice to always specify this argument.
`train`	A matrix-like R object (e.g., a data frame or matrix) containing the training data. If `NULL` (the default) then the internal 'get_training_data()' function will be called to try and extract it automatically. It is good practice to always specify this argument.

Details

This approach to computing VI scores is based on the mean absolute value of the SHAP values for each feature; see, for example, https://github.com/slundberg/shap and the references therein.

Strumbelj, E., and Kononenko, I. Explaining prediction models and individual predictions with feature contributions. Knowledge and information systems 41.3 (2014): 647-665.

Value

A tidy data frame (i.e., a "tibble" object) with two columns, Variable and Importance, containing the variable name and its associated importance score, respectively.

vip

Variable Importance Plots

v0.3.2

GPL (>= 2)

Authors

Brandon Greenwell [aut, cre] (<https://orcid.org/0000-0002-8120-0084>), Brad Boehmke [aut] (<https://orcid.org/0000-0002-3611-8516>), Bernie Gray [aut] (<https://orcid.org/0000-0001-9190-6032>)

Initial release