42 lines
1.8 KiB
R
42 lines
1.8 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/xgb.plot.importance.R
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\name{xgb.plot.importance}
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\alias{xgb.plot.importance}
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\title{Plot feature importance bar graph}
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\usage{
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xgb.plot.importance(importance_matrix = NULL, numberOfClusters = c(1:10))
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}
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\arguments{
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\item{importance_matrix}{a \code{data.table} returned by the \code{xgb.importance} function.}
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\item{numberOfClusters}{a \code{numeric} vector containing the min and the max range of the possible number of clusters of bars.}
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}
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\value{
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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.
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}
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\description{
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Read a data.table containing feature importance details and plot it.
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}
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\details{
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The purpose of this function is to easily represent the importance of each feature of a model.
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The function return a ggplot graph, therefore each of its characteristic can be overriden (to customize it).
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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.
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}
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\examples{
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data(agaricus.train, package='xgboost')
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#Both dataset are list with two items, a sparse matrix and labels
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#(labels = outcome column which will be learned).
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#Each column of the sparse Matrix is a feature in one hot encoding format.
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train <- agaricus.train
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bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
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#train$data@Dimnames[[2]] represents the column names of the sparse matrix.
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importance_matrix <- xgb.importance(train$data@Dimnames[[2]], model = bst)
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xgb.plot.importance(importance_matrix)
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}
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