Plot Permutation Variable Importances. — h2o.permutation_importance

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

Usage

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

if (FALSE) { # \dontrun{
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)
} # }