`R/permutation_varimp.R`

`h2o.permutation_importance_plot.Rd`

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 )

object | A trained supervised H2O model. |
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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). |

H2OTable with variable importance.

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) }