Plot Permutation Variable Importances.

This method plots either a bar plot or if n_repeats > 1 a box plot and returns the variable importance table.

h2o.permutation_importance_plot(
  object,
  newdata,
  metric = c("AUTO", "AUC", "MAE", "MSE", "RMSE", "logloss", "mean_per_class_error",
    "PR_AUC"),
  n_samples = 10000,
  n_repeats = 1,
  features = NULL,
  seed = -1,
  num_of_features = NULL
)

Arguments

object	A trained supervised H2O model.
newdata	Training frame of the model which is going to be permuted
metric	Metric to be used. One of "AUTO", "AUC", "MAE", "MSE", "RMSE", "logloss", "mean_per_class_error", "PR_AUC". Defaults to "AUTO".
n_samples	Number of samples to be evaluated. Use -1 to use the whole dataset. Defaults to 10 000.
n_repeats	Number of repeated evaluations. Defaults to 1.
features	Character vector of features to include in the permutation importance. Use NULL to include all.
seed	Seed for the random generator. Use -1 to pick a random seed. Defaults to -1.
num_of_features	The number of features shown in the plot (default is 10 or all if less than 10).

Value

H2OTable with variable importance.

Examples

# NOT RUN {
library(h2o)
h2o.init()
prostate_path <- system.file("extdata", "prostate.csv", package = "h2o")
prostate <- h2o.importFile(prostate_path)
prostate[, 2] <- as.factor(prostate[, 2])
model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli")
h2o.permutation_importance_plot(model, prostate)
# }