gpb.importance {gpboost}

Compute feature importance in a model

Description

Creates a data.table of feature importances in a model.

Usage

gpb.importance(model, percentage = TRUE)

Arguments

model

object of class gpb.Booster.

percentage

whether to show importance in relative percentage.

Value

For a tree model, a data.table with the following columns:

• Feature: Feature names in the model.

• Gain: The total gain of this feature's splits.

• Cover: The number of observation related to this feature.

• Frequency: The number of times a feature splited in trees.

Examples

data(agaricus.train, package = "gpboost")
train <- agaricus.train
dtrain <- gpb.Dataset(train$data, label = train$label)

params <- list(
  objective = "binary"
  , learning_rate = 0.1
  , max_depth = -1L
  , min_data_in_leaf = 1L
  , min_sum_hessian_in_leaf = 1.0
)
model <- gpb.train(
    params = params
    , data = dtrain
    , nrounds = 5L
)

tree_imp1 <- gpb.importance(model, percentage = TRUE)
tree_imp2 <- gpb.importance(model, percentage = FALSE)