R: Plot feature contribution as a bar graph

lgb.plot.interpretation {lightgbm} | R Documentation

## Plot feature contribution as a bar graph

### Description

Plot previously calculated feature contribution as a bar graph.

### Usage

    lgb.plot.interpretation(
      tree_interpretation_dt,
      top_n = 10L,
      cols = 1L,
      left_margin = 10L,
      cex = NULL
    )

### Arguments

`tree_interpretation_dt` | a `data.table` returned by `lgb.interprete`.
`top_n` | maximal number of top features to include into the plot.
`cols` | the column numbers of layout, will be used only for multiclass classification feature contribution.
`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`.

### Details

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

### Value

The `lgb.plot.interpretation` function creates a `barplot`.

### Examples

    Logit <- function(x) {
      log(x / (1.0 - x))
    }
    data(agaricus.train, package = "lightgbm")
    labels <- agaricus.train$label
    dtrain <- lgb.Dataset(
      agaricus.train$data
      , label = labels
    )
    setinfo(dtrain, "init_score", rep(Logit(mean(labels)), length(labels)))

    data(agaricus.test, package = "lightgbm")

    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_interpretation <- lgb.interprete(
      model = model
      , data = agaricus.test$data
      , idxset = 1L:5L
    )
    lgb.plot.interpretation(
      tree_interpretation_dt = tree_interpretation[[1L]]
      , top_n = 3L
    )

[Package lightgbm version 3.1.0 Index]