`R/measure_importance.R`

`important_variables.Rd`

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

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

importance_frame | A result of using the function measure_importance() to a random forest or a randomForest object |
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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. |

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

forest <- randomForest::randomForest(Species ~ ., data = iris, localImp = TRUE, ntree = 300) important_variables(measure_importance(forest), k = 2)#> [1] "Petal.Length" "Petal.Width"