| lgb.plot.interpretation {lightgbm} | R Documentation |
Plot previously calculated feature contribution as a bar graph.
lgb.plot.interpretation(tree_interpretation_dt, top_n = 10, cols = 1, left_margin = 10, cex = NULL)
tree_interpretation_dt |
a |
top_n |
maximal number of top features to include into the plot. |
cols |
the column numbers of layout, will be used only for multiclass classification feature contribution. |
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 contribution of a feature. Features are shown ranked in a decreasing contribution order.
The lgb.plot.interpretation function creates a barplot.
library(lightgbm)
Sigmoid <- function(x) {1 / (1 + exp(-x))}
Logit <- function(x) {log(x / (1 - x))}
data(agaricus.train, package = "lightgbm")
train <- agaricus.train
dtrain <- lgb.Dataset(train$data, label = train$label)
setinfo(dtrain, "init_score", rep(Logit(mean(train$label)), length(train$label)))
data(agaricus.test, package = "lightgbm")
test <- agaricus.test
params <- list(objective = "binary",
learning_rate = 0.01, num_leaves = 63, max_depth = -1,
min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1)
model <- lgb.train(params, dtrain, 20)
model <- lgb.train(params, dtrain, 20)
tree_interpretation <- lgb.interprete(model, test$data, 1:5)
lgb.plot.interpretation(tree_interpretation[[1]], top_n = 10)