important_variables: Extract k most important variables in a random forest

In ModelOriented/randomForestExplainer: Explaining and Visualizing Random Forests in Terms of Variable Importance

Extract k most important variables in a random forest

Description

Get the names of k variables with highest sum of rankings based on the specified importance measures

Usage

important_variables(
  importance_frame,
  k = 15,
  measures = names(importance_frame)[2:min(5, ncol(importance_frame))],
  ties_action = "all"
)

Arguments

importance_frame	A result of using the function measure_importance() to a random forest or a randomForest object
k	The number of variables to extract
measures	A character vector specifying the measures of importance to be used
ties_action	One of three: c("none", "all", "draw"); specifies which variables to pick when ties occur. When set to "none" we may get less than k variables, when "all" we may get more and "draw" makes us get exactly k.

Value

A character vector with names of k variables with highest sum of rankings

Examples

forest <- randomForest::randomForest(Species ~ ., data = iris, localImp = TRUE, ntree = 300)
important_variables(measure_importance(forest), k = 2)

ModelOriented/randomForestExplainer documentation built on March 23, 2024, 10:31 p.m.