View source: R/lgb.plot.interpretation.R
lgb.plot.interpretation | R Documentation |
Plot previously calculated feature contribution as a bar graph.
lgb.plot.interpretation( tree_interpretation_dt, top_n = 10L, cols = 1L, left_margin = 10L, 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
.
Logit <- function(x) { log(x / (1.0 - x)) } data(agaricus.train, package = "lightgbm") labels <- agaricus.train$label dtrain <- lgb.Dataset( agaricus.train$data , label = labels ) set_field( dataset = dtrain , field_name = "init_score" , data = rep(Logit(mean(labels)), length(labels)) ) data(agaricus.test, package = "lightgbm") params <- list( objective = "binary" , learning_rate = 0.1 , max_depth = -1L , min_data_in_leaf = 1L , min_sum_hessian_in_leaf = 1.0 ) model <- lgb.train( params = params , data = dtrain , nrounds = 5L ) tree_interpretation <- lgb.interprete( model = model , data = agaricus.test$data , idxset = 1L:5L ) lgb.plot.interpretation( tree_interpretation_dt = tree_interpretation[[1L]] , top_n = 3L )
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.