gpboost (version 1.5.6)

gpb.plot.interpretation: Plot feature contribution as a bar graph

Description

Plot previously calculated feature contribution as a bar graph.

Usage

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

Value

The gpb.plot.interpretation function creates a barplot.

Arguments

tree_interpretation_dt

a data.table returned by gpb.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.

Examples

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

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

params <- list(
  objective = "binary"
  , learning_rate = 0.1
  , max_depth = -1L
  , min_data_in_leaf = 1L
  , min_sum_hessian_in_leaf = 1.0
)
model <- gpb.train(
  params = params
  , data = dtrain
  , nrounds = 5L
)

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