R: Compute feature importance in a model

lgb.importance {lightgbm} | R Documentation

## 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)

[Package lightgbm version 3.3.2 Index]