randomForestExplainer (version 0.10.1)

important_variables: 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

# NOT RUN {
forest <- randomForest::randomForest(Species ~ ., data = iris, localImp = TRUE, ntree = 300)
important_variables(measure_importance(forest), k = 2)

# }