View source: R/permutation_varimp.R
h2o.permutation_importance_plot | R Documentation |
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. |
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.
## 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)
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