% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.plot.importance.R
\name{xgb.plot.importance}
\alias{xgb.plot.importance}
\title{Plot feature importance bar graph}
\usage{
xgb.plot.importance(importance_matrix = NULL, n_clusters = c(1:10), ...)
}
\arguments{
\item{importance_matrix}{a \code{data.table} returned by the \code{xgb.importance} function.}

\item{n_clusters}{a \code{numeric} vector containing the min and the max range of the possible number of clusters of bars.}

\item{...}{currently not used}
}
\value{
A \code{ggplot2} bar graph representing each feature by a horizontal bar. Longer is the bar, more important is the feature. Features are classified by importance and clustered by importance. The group is represented through the color of the bar.
}
\description{
Read a data.table containing feature importance details and plot it (for both GLM and Trees).
}
\details{
The purpose of this function is to easily represent the importance of each feature of a model.
The function returns a ggplot graph, therefore each of its characteristic can be overriden (to customize it).
In particular you may want to override the title of the graph. To do so, add \code{+ ggtitle("A GRAPH NAME")} next to the value returned by this function.
}
\examples{
data(agaricus.train, package='xgboost')

#Both dataset are list with two items, a sparse matrix and labels
#(labels = outcome column which will be learned).
#Each column of the sparse Matrix is a feature in one hot encoding format.

bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")

importance_matrix <- xgb.importance(colnames(agaricus.train$data), model = bst)
xgb.plot.importance(importance_matrix)

}