115 lines
3.7 KiB
R
115 lines
3.7 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}
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\usage{
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xgb.ggplot.importance(
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importance_matrix = NULL,
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top_n = NULL,
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measure = NULL,
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rel_to_first = FALSE,
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n_clusters = seq_len(10),
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...
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)
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xgb.plot.importance(
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importance_matrix = NULL,
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top_n = NULL,
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measure = NULL,
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rel_to_first = FALSE,
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left_margin = 10,
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cex = NULL,
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plot = TRUE,
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...
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)
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}
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\arguments{
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\item{importance_matrix}{A \code{data.table} as returned by \code{\link[=xgb.importance]{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
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the highest ranked feature, see Details.}
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\item{n_clusters}{A 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{\link[graphics:barplot]{graphics::barplot()}}
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(except \code{horiz}, \code{border}, \code{cex.names}, \code{names.arg}, and \code{las}).
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Only used in \code{xgb.plot.importance()}.}
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\item{left_margin}{Adjust the left margin size to fit feature names.
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When \code{NULL}, the existing \code{par("mar")} is used.}
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\item{cex}{Passed as \code{cex.names} parameter to \code{\link[graphics:barplot]{graphics::barplot()}}.}
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\item{plot}{Should the barplot be shown? Default is \code{TRUE}.}
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}
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\value{
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The return value depends on the function:
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\itemize{
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\item \code{xgb.plot.importance()}: Invisibly, a "data.table" with \code{n_top} features sorted
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by importance. If \code{plot = TRUE}, the values are also plotted as barplot.
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\item \code{xgb.ggplot.importance()}: A customizable "ggplot" object.
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E.g., to change the title, set \code{+ ggtitle("A GRAPH NAME")}.
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}
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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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\itemize{
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\item \code{xgb.plot.importance()} uses base R graphics, while
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\item \code{xgb.ggplot.importance()} uses "ggplot".
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}
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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
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importance of a feature. Features are sorted by decreasing importance.
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It works for both "gblinear" and "gbtree" models.
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When \code{rel_to_first = FALSE}, the values would be plotted as in \code{importance_matrix}.
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For a "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 performs 1-D clustering of the importance values,
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with bar colors corresponding to different clusters having similar importance values.
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}
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\examples{
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data(agaricus.train)
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## Keep the number of threads to 2 for examples
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nthread <- 2
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data.table::setDTthreads(nthread)
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bst <- xgb.train(
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data = xgb.DMatrix(agaricus.train$data, label = agaricus.train$label),
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max_depth = 3,
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eta = 1,
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nthread = nthread,
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nrounds = 2,
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objective = "binary:logistic"
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)
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importance_matrix <- xgb.importance(colnames(agaricus.train$data), model = bst)
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xgb.plot.importance(
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importance_matrix, rel_to_first = TRUE, xlab = "Relative importance"
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)
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gg <- xgb.ggplot.importance(
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importance_matrix, measure = "Frequency", rel_to_first = TRUE
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)
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gg
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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]{graphics::barplot()}}
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}
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