wehub-resource-sync
65 lines
1.9 KiB
R
65 lines
1.9 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/lgb.plot.importance.R
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\name{lgb.plot.importance}
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\alias{lgb.plot.importance}
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\title{Plot feature importance as a bar graph}
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\usage{
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lgb.plot.importance(
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tree_imp,
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top_n = 10L,
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measure = "Gain",
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left_margin = 10L,
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cex = NULL
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)
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}
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\arguments{
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\item{tree_imp}{a \code{data.table} returned by \code{\link{lgb.importance}}.}
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\item{top_n}{maximal number of top features to include into the plot.}
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\item{measure}{the name of importance measure to plot, can be "Gain", "Cover" or "Frequency".}
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\item{left_margin}{(base R barplot) allows to adjust the left margin size to fit feature names.}
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\item{cex}{(base R barplot) passed as \code{cex.names} parameter to \code{\link[graphics]{barplot}}.
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Set a number smaller than 1.0 to make the bar labels smaller than R's default and values
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greater than 1.0 to make them larger.}
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}
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\value{
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The \code{lgb.plot.importance} function creates a \code{barplot}
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and silently returns a processed data.table with \code{top_n} features sorted by defined importance.
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}
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\description{
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Plot previously calculated feature importance: Gain, Cover and Frequency, as a bar graph.
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}
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\details{
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The graph represents each feature as a horizontal bar of length proportional to the defined importance of a feature.
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Features are shown ranked in a decreasing importance order.
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}
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\examples{
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\donttest{
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\dontshow{setLGBMthreads(2L)}
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\dontshow{data.table::setDTthreads(1L)}
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data(agaricus.train, package = "lightgbm")
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train <- agaricus.train
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dtrain <- lgb.Dataset(train$data, label = train$label)
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params <- list(
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objective = "binary"
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, learning_rate = 0.1
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, min_data_in_leaf = 1L
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, min_sum_hessian_in_leaf = 1.0
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, num_threads = 2L
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)
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model <- lgb.train(
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params = params
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, data = dtrain
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, nrounds = 5L
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)
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tree_imp <- lgb.importance(model, percentage = TRUE)
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lgb.plot.importance(tree_imp, top_n = 5L, measure = "Gain")
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}
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}
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