Compute feature importance in a model

Source: R/lgb.importance.R

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

lgb.importance(model, percentage = TRUE)

Arguments

model

object of class lgb.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

library(lightgbm)
data(agaricus.train, package = "lightgbm")
train <- agaricus.train
dtrain <- lgb.Dataset(train$data, label = train$label)

params <- list(objective = "binary",
              learning_rate = 0.01, num_leaves = 63, max_depth = -1,
              min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1)
              model <- lgb.train(params, dtrain, 20)
model <- lgb.train(params, dtrain, 20)

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