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.
# \donttest{ setLGBMthreads(2L) data.table::setDTthreads(1L) 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 , num_threads = 2L ) model <- lgb.train( params = params , data = dtrain , nrounds = 5L ) #> [LightGBM] [Info] Number of positive: 3140, number of negative: 3373 #> [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000886 seconds. #> You can set `force_row_wise=true` to remove the overhead. #> And if memory is not enough, you can set `force_col_wise=true`. #> [LightGBM] [Info] Total Bins 232 #> [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116 #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580 #> [LightGBM] [Info] Start training from score -0.071580 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf tree_imp1 <- lgb.importance(model, percentage = TRUE) tree_imp2 <- lgb.importance(model, percentage = FALSE) # }