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 = 10, cols = 1,
      left_margin = 10, 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

    library(lightgbm)
    Sigmoid <- function(x) {1 / (1 + exp(-x))}
    Logit <- function(x) {log(x / (1 - x))}
    data(agaricus.train, package = "lightgbm")
    train <- agaricus.train
    dtrain <- lgb.Dataset(train$data, label = train$label)
    setinfo(dtrain, "init_score", rep(Logit(mean(train$label)), length(train$label)))
    data(agaricus.test, package = "lightgbm")
    test <- agaricus.test

    params <- list(objective = "binary",
                  learning_rate = 0.01, num_leaves = 63, max_depth = -1,
                  min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1)
                  model <- lgb.train(params, dtrain, 20)
    model <- lgb.train(params, dtrain, 20)

    tree_interpretation <- lgb.interprete(model, test$data, 1:5)
    lgb.plot.interpretation(tree_interpretation[[1]], top_n = 10)

[Package lightgbm version 2.2.2 Index]