xgb.ggplot.importance {xgboost} | R Documentation

## Plot feature importance as a bar graph

### Description

Represents previously calculated feature importance as a bar graph. `xgb.plot.importance` uses base R graphics, while `xgb.ggplot.importance` uses the ggplot backend.

### Usage

    xgb.ggplot.importance(importance_matrix = NULL, top_n = NULL,
      measure = NULL, rel_to_first = FALSE, n_clusters = c(1:10), ...)

    xgb.plot.importance(importance_matrix = NULL, top_n = NULL,
      measure = NULL, rel_to_first = FALSE, left_margin = 10, cex = NULL,
      plot = TRUE, ...)

### Arguments

`importance_matrix` | a `data.table` returned by `xgb.importance`.
`top_n` | maximal number of top features to include into the plot.
`measure` | the name of importance measure to plot. When `NULL`, 'Gain' would be used for trees and 'Weight' would be used for gblinear.
`rel_to_first` | whether importance values should be represented as relative to the highest ranked feature. See Details.
`n_clusters` | (ggplot only) a `numeric` vector containing the min and the max range of the possible number of clusters of bars.
`...` | other parameters passed to `barplot` (except horiz, border, cex.names, names.arg, and las).
`left_margin` | (base R barplot) allows to adjust the left margin size to fit feature names. When it is NULL, the existing `par('mar')` is used.
`cex` | (base R barplot) passed as `cex.names` parameter to `barplot`.
`plot` | (base R barplot) whether a barplot should be produced. If FALSE, only a data.table is returned.

### Details

The graph represents each feature as a horizontal bar of length proportional to the importance of a feature. Features are shown ranked in a decreasing importance order. It works for importances from both `gblinear` and `gbtree` models.

When `rel_to_first = FALSE`, the values would be plotted as they were in `importance_matrix`. For gbtree model, that would mean being normalized to the total of 1 ("what is feature's importance contribution relative to the whole model?"). For linear models, `rel_to_first = FALSE` would show actual values of the coefficients. Setting `rel_to_first = TRUE` allows to see the picture from the perspective of "what is feature's importance contribution relative to the most important feature?"

The ggplot-backend method also performs 1-D clustering of the importance values, with bar colors corresponding to different clusters that have somewhat similar importance values.

### Value

The `xgb.plot.importance` function creates a `barplot` (when `plot=TRUE`) and silently returns a processed data.table with `n_top` features sorted by importance.

The `xgb.ggplot.importance` function returns a ggplot graph which could be customized afterwards. E.g., to change the title of the graph, add `+ ggtitle("A GRAPH NAME")` to the result.

### See Also

`barplot`.

### Examples

    data(agaricus.train)

    bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
                   eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")

    importance_matrix <- xgb.importance(colnames(agaricus.train$data), model = bst)

    xgb.plot.importance(importance_matrix, rel_to_first = TRUE, xlab = "Relative importance")

    (gg <- xgb.ggplot.importance(importance_matrix, measure = "Frequency", rel_to_first = TRUE))
    gg + ggplot2::ylab("Frequency")

[Package xgboost version 0.90.0.2 Index]