lgb.importance: Compute feature importance in a model

In lightgbm: Light Gradient Boosting Machine

Compute feature importance in a model

Description

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

Usage

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

data(agaricus.train, package = "lightgbm")
train <- agaricus.train
dtrain <- lgb.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 <- lgb.train(
    params = params
    , data = dtrain
    , nrounds = 5L
)

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

lightgbm documentation built on Jan. 17, 2023, 1:13 a.m.