xgb.plot.importance
From xgboost v0.44
by Tong He
Plot feature importance bar graph
Read a data.table containing feature importance details and plot it.
Usage
xgb.plot.importance(importance_matrix = NULL, numberOfClusters = c(1:10))
Arguments
 importance_matrix
 a
data.table
returned by thexgb.importance
function.  numberOfClusters
 a
numeric
vector containing the min and the max range of the possible number of clusters of bars.
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 + ggtitle("A GRAPH NAME")
next to the value returned by this function.
Value

A
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.
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, nthread = 2, 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)
Community examples
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