Compute ICE-based variable importance scores for the predictors in a model. (This function is meant for internal use only.)
vi_ice(object, ...)# S3 method for default
vi_ice(object, feature_names, FUN = NULL, ...)
A fitted model object (e.g., a "randomForest" object).
Additional optional arguments to be passed onto
partial.
Character string giving the names of the predictor variables (i.e., features) of interest.
List with two components, "cat" and "con",
containing the functions to use for categorical and continuous features,
respectively. If NULL, the standard deviation is used for continuous
features. For categorical features, the range statistic is used (i.e.,
(max - min) / 4).
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.
Coming soon!