Plot feature importance as a bar graph

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

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.

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.

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.

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")
#> [1]	train-error:0.014433 
#> [2]	train-error:0.001228 

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))
#> Error: Ckmeans.1d.dp package is required
gg + ggplot2::ylab("Frequency")
#> Error in eval(expr, envir, enclos): object 'gg' not found