Automated H2O's AutoML Results — h2o

This is an auxiliary function to calculate predictions and results when using the h2o_automl() function.

Usage

h2o_results(
  h2o_object,
  test,
  train,
  y = "tag",
  which = 1,
  model_type,
  target = "auto",
  split = 0.7,
  ignore = NULL,
  quiet = FALSE,
  project = "ML Project",
  seed = 0,
  leaderboard = list(),
  plots = TRUE,
  ...
)

Arguments

h2o_object: H2O Leaderboard (H2OFrame/H2OAutoML) or Model (h2o)
test, train: Dataframe. Must have the same columns
y: Variable or Character. Name of the dependent variable or response.
which: Integer. Which model to select from leaderboard
model_type: Character. Select "Classification" or "Regression"
target: Value. Which is your target positive value? If set to 'auto', the target with largest mean(score) will be selected. Change the value to overwrite. Only used when binary categorical model.
split: Numeric. Value between 0 and 1 to split as train/test datasets. Value is for training set. Set value to 1 to train with all available data and test with same data (cross-validation will still be used when training). If train_test is set, value will be overwritten with its real split rate.
ignore: Character vector. Columns too ignore
quiet: Boolean. Quiet all messages, warnings, recommendations?
project: Character. Your project's name
seed: Integer. Set a seed for reproducibility. AutoML can only guarantee reproducibility if max_models is used because max_time is resource limited.
leaderboard: H2O's Leaderboard. Passed when using h2o_selectmodel as it contains plain model and no leader board.
plots: Boolean. Create plots objects?
...: Additional parameters on h2o::h2o.automl

Value

List. Trained model, predicted scores and datasets used, performance metrics, parameters, importance data.frame, seed, and plots when plots=TRUE.