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
)

Arguments

tree_interpretation_dt

a data.table returned by lgb.interprete.

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 cex.names parameter to barplot.

Value

The lgb.plot.interpretation function creates a barplot.

Details

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.

Examples

# \dontrun{ 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 ) setinfo(dtrain, "init_score", 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 )
#> [LightGBM] [Info] Number of positive: 3140, number of negative: 3373 #> [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001054 seconds. #> You can set `force_row_wise=true` to remove the overhead. #> And if memory is not enough, you can set `force_col_wise=true`. #> [LightGBM] [Info] Total Bins 232 #> [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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 )
# }