gpb.plot.importance {gpboost}

Plot feature importance as a bar graph

Description

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

Usage

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

Arguments

tree_imp

a data.table returned by gpb.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.

...

other parameters passed to graphics::barplot

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 gpb.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 = "gpboost")
train <- agaricus.train
dtrain <- gpb.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 <- gpb.train(
    params = params
    , data = dtrain
    , nrounds = 5L
)

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