# xgb.plot.importance

From xgboost v0.4-2
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 the`xgb.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)
```

*Documentation reproduced from package xgboost, version 0.4-2, License: Apache License (== 2.0) | file LICENSE*

### Community examples

Looks like there are no examples yet.