Builds a Driverless AI model

If not specified, the optional parameters are determined by the Driverless AI platform, see also dai.suggest_model_params.

dai.train(training_frame, target_col, is_classification, is_timeseries,
  testing_frame = NULL, validation_frame = NULL,
  resumed_model = NULL, weight_col = NULL, fold_col = NULL,
  time_col = NULL, scorer = NULL, cols_to_drop = NULL,
  accuracy = NULL, time = NULL, interpretability = NULL,
  time_groups_columns = NULL, time_period_in_seconds = NULL,
  num_prediction_periods = NULL, num_gap_periods = NULL,
  enable_gpus = NULL, config_overrides = NULL, seed = NULL,
  progress = getOption("dai.progress", TRUE))

Arguments

training_frame	DAIFrame to use to build the model.
target_col	The name of the target variable.
is_classification	Whether the predicted variable is categorical (TRUE) or numerical (FALSE).
is_timeseries	Whether the target variable is a time-series or not.
testing_frame	DAIFrame to evaluate the model on at the end. It is not used for the model training (optional).
validation_frame	DAIFrame to use for the model validation during the model training (optional).
resumed_model	Model or model key used for retraining/re-ensembling/starting from checkpoint (optional).
weight_col	Weights column name (optional).
fold_col	Fold column name (optional).
time_col	Time column name, containing time ordering for timeseries problems (optional).
scorer	Name of one of the available scorers (optional).
cols_to_drop	A character vector of column names to be dropped from the data (optional).
accuracy	Accuracy setting [1-10] (optional).
time	Time setting [1-10] (optional).
interpretability	Interpretability setting [1-10] (optional).
time_groups_columns	List of column names, contributing to time ordering (optional).
time_period_in_seconds	Size of Lag features in seconds, used in timeseries problems (optional).
num_prediction_periods	Timeseries forecast horizont in time period units (optional).
num_gap_periods	Number of time periods after which forecast starts (optional).
enable_gpus	Whether to use GPUs (optional).
config_overrides	DriverlessAI config overrides for separate experiment in TOML string format (optional).
seed	The random number generator's seed (optional).
progress	Whether to display a progress bar (optional).

Value

DAIModel

See also

dai.suggest_model_params

Examples

# NOT RUN {
dai.connect(uri = 'http://127.0.0.1:12345', username = 'h2oai', password = 'h2oai')
iris_dai <- as.DAIFrame(iris, progress = FALSE)
# Simple model with minimal parameters
simple_model <- dai.train(training_frame = iris_dai,
                          target_col = 'Species',
                          is_classification = TRUE,
                          is_timeseries = FALSE,
                          time = 1, accuracy = 1, interpretability = 10,
                          progress = FALSE)
print(simple_model)
# }# NOT RUN {
# More complex model that may take more time to train
model <- dai.train(training_frame = iris_dai,
                   target_col = 'Species',
                   is_classification = TRUE,
                   is_timeseries = FALSE,
                   time = 10, accuracy = 10, interpretability = 5,
                   progress = FALSE)
print(model)
# Custom config to enable compliant recipe (see config.toml for more details)
compliant_model <- dai.train(training_frame = iris_dai,
                             target_col = 'Species',
                             is_classification = TRUE,
                             is_timeseries = FALSE,
                             time = 10, accuracy = 10,
                             config_overrides = "recipe = 'compliant'",
                             progress = FALSE)
print(compliant_model)
# }