new plot feature importance function

R-package/man/xgb.plot.importance.Rd

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+% 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)
+}
+