h2o.permutation_importance_plot function

Plot Permutation Variable Importances.

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).

Returns

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) ## End(Not run)
  • Maintainer: Tomas Fryda
  • License: Apache License (== 2.0)
  • Last published: 2024-01-11