% Generated by roxygen2 (4.1.0): 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, numberOfClusters = c(1:10))
}
\arguments{
\item{importance_matrix}{a \code{data.table} returned by the \code{xgb.importance} function.}

\item{numberOfClusters}{a \code{numeric} vector containing the min and the max range of the possible number of clusters of bars.}
}
\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.
}
\details{
The purpose of this function is to easily represent the importance of each feature of a model.
The function return 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.
train <- agaricus.train

bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
               eta = 1, nround = 2,objective = "binary:logistic")

#train$data@Dimnames[[2]] represents the column names of the sparse matrix.
importance_matrix <- xgb.importance(train$data@Dimnames[[2]], model = bst)
xgb.plot.importance(importance_matrix)
}