Plot feature importance as a bar graph
Plot previously calculated feature importance: Gain, Cover and Frequency, as a bar graph.
lgb.plot.importance( tree_imp, top_n = 10L, measure = "Gain", left_margin = 10L, cex = NULL )
tree_imp |
a |
top_n |
maximal number of top features to include into the plot. |
measure |
the name of importance measure to plot, can be "Gain", "Cover" or "Frequency". |
left_margin |
(base R barplot) allows to adjust the left margin size to fit feature names. |
cex |
(base R barplot) passed as |
The graph represents each feature as a horizontal bar of length proportional to the defined importance of a feature. Features are shown ranked in a decreasing importance order.
The lgb.plot.importance function creates a barplot
and silently returns a processed data.table with top_n features sorted by defined importance.
data(agaricus.train, package = "lightgbm")
train <- agaricus.train
dtrain <- lgb.Dataset(train$data, label = train$label)
params <- list(
objective = "binary"
, learning_rate = 0.1
, min_data_in_leaf = 1L
, min_sum_hessian_in_leaf = 1.0
)
model <- lgb.train(
params = params
, data = dtrain
, nrounds = 5L
)
tree_imp <- lgb.importance(model, percentage = TRUE)
lgb.plot.importance(tree_imp, top_n = 5L, measure = "Gain")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.