Compute feature importance in a model
Creates a data.table of feature importances in a model.
lgb.importance(model, percentage = TRUE)
model |
object of class |
percentage |
whether to show importance in relative percentage. |
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.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.