R: Plot feature importance as a bar graph

lgb.plot.importance {lightgbm} | R Documentation

## Plot feature importance as a bar graph

### Description

Plot previously calculated feature importance: Gain, Cover and Frequency, as a bar graph.

### Usage

    lgb.plot.importance(
      tree_imp,
      top_n = 10L,
      measure = "Gain",
      left_margin = 10L,
      cex = NULL
    )

### Arguments

`tree_imp` | a `data.table` returned by `lgb.importance`.
`top_n` | maximal number of top features to include into the plot.
`measure` | the name of importance measure to plot, can be "Gain", "Cover" or "Frequency".
`left_margin` | (base R barplot) allows to adjust the left margin size to fit feature names.
`cex` | (base R barplot) passed as `cex.names` parameter to `barplot`. Set a number smaller than 1.0 to make the bar labels smaller than R's default and values greater than 1.0 to make them larger.

### Details

The graph represents each feature as a horizontal bar of length proportional to the defined importance of a feature. Features are shown ranked in a decreasing importance order.

### Value

The `lgb.plot.importance` function creates a `barplot` and silently returns a processed data.table with `top_n` features sorted by defined importance.

### 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
        , min_data_in_leaf = 1L
        , min_sum_hessian_in_leaf = 1.0
    )

    model <- lgb.train(
        params = params
        , data = dtrain
        , nrounds = 5L
    )

    tree_imp <- lgb.importance(model, percentage = TRUE)
    lgb.plot.importance(tree_imp, top_n = 5L, measure = "Gain")

[Package lightgbm version 3.3.2 Index]