xgboost (version 0.4-2)

xgb.plot.importance: Plot feature importance bar graph

Description

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 the xgb.importance function.

numberOfClusters

a numeric vector containing the min and the max range of the possible number of clusters of bars.

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.

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.

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)