h2o (version 3.38.0.1)

h2o.permutation_importance_plot: Plot Permutation Variable Importances.

Description

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
)

Value

H2OTable with variable importance.

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).

Examples

if (FALSE) {
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)
}