b4d97d3cb8
* [R] better argument check in xgb.DMatrix; fixes #1480 * [R] showsd was a dummy; fixes #2044 * [R] better categorical encoding explanation in vignette; fixes #1989 * [R] new roxygen version docs update
82 lines
3.6 KiB
R
82 lines
3.6 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/xgb.ggplot.R, R/xgb.plot.importance.R
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\name{xgb.ggplot.importance}
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\alias{xgb.ggplot.importance}
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\alias{xgb.plot.importance}
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\title{Plot feature importance as a bar graph}
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\usage{
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xgb.ggplot.importance(importance_matrix = NULL, top_n = NULL,
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measure = NULL, rel_to_first = FALSE, n_clusters = c(1:10), ...)
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xgb.plot.importance(importance_matrix = NULL, top_n = NULL,
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measure = NULL, rel_to_first = FALSE, left_margin = 10, cex = NULL,
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plot = TRUE, ...)
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}
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\arguments{
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\item{importance_matrix}{a \code{data.table} returned by \code{\link{xgb.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.
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When \code{NULL}, 'Gain' would be used for trees and 'Weight' would be used for gblinear.}
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\item{rel_to_first}{whether importance values should be represented as relative to the highest ranked feature.
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See Details.}
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\item{n_clusters}{(ggplot only) a \code{numeric} vector containing the min and the max range
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of the possible number of clusters of bars.}
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\item{...}{other parameters passed to \code{barplot} (except horiz, border, cex.names, names.arg, and las).}
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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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When it is NULL, the existing \code{par('mar')} is used.}
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\item{cex}{(base R barplot) passed as \code{cex.names} parameter to \code{barplot}.}
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\item{plot}{(base R barplot) whether a barplot should be produced.
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If FALSE, only a data.table is returned.}
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}
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\value{
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The \code{xgb.plot.importance} function creates a \code{barplot} (when \code{plot=TRUE})
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and silently returns a processed data.table with \code{n_top} features sorted by importance.
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The \code{xgb.ggplot.importance} function returns a ggplot graph which could be customized afterwards.
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E.g., to change the title of the graph, add \code{+ ggtitle("A GRAPH NAME")} to the result.
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}
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\description{
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Represents previously calculated feature importance as a bar graph.
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\code{xgb.plot.importance} uses base R graphics, while \code{xgb.ggplot.importance} uses the ggplot backend.
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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 importance of a feature.
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Features are shown ranked in a decreasing importance order.
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It works for importances from both \code{gblinear} and \code{gbtree} models.
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When \code{rel_to_first = FALSE}, the values would be plotted as they were in \code{importance_matrix}.
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For gbtree model, that would mean being normalized to the total of 1
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("what is feature's importance contribution relative to the whole model?").
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For linear models, \code{rel_to_first = FALSE} would show actual values of the coefficients.
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Setting \code{rel_to_first = TRUE} allows to see the picture from the perspective of
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"what is feature's importance contribution relative to the most important feature?"
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The ggplot-backend method also performs 1-D custering of the importance values,
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with bar colors coresponding to different clusters that have somewhat similar importance values.
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}
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\examples{
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data(agaricus.train)
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bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
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eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
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importance_matrix <- xgb.importance(colnames(agaricus.train$data), model = bst)
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xgb.plot.importance(importance_matrix, rel_to_first = TRUE, xlab = "Relative importance")
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(gg <- xgb.ggplot.importance(importance_matrix, measure = "Frequency", rel_to_first = TRUE))
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gg + ggplot2::ylab("Frequency")
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}
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\seealso{
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\code{\link[graphics]{barplot}}.
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}
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