xgb.ggplot.importance
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.
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.tablereturned byxgb.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
numericvector 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.namesparameter tobarplot.- 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 custering of the importance values, with bar colors coresponding 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
Examples
# NOT RUN {
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")
# }