Compute feature importance in a model

Description

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

Usage

lgb.importance(model, percentage = TRUE)

Arguments

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 = 63L
  , max_depth = -1L
  , min_data_in_leaf = 1L
  , min_sum_hessian_in_leaf = 1.0
)
model <- lgb.train(params, dtrain, 10L)

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