automl_reg() is a way to generate a specification of a AutoML model before fitting and allows the model to be created using different packages. Currently the only package is h2o.

automl_reg(mode = "regression")



A single character string for the type of model. The only possible value for this model is "regression".


An updated model specification with classes automl_reg and model_spec.


Other options and arguments can be set using set_engine().

The model can be created using the fit() function using the following engines:

  • H2O "h2o" (the default)



The engine uses h2o.automl().

Fit Details

The following features are REQUIRED to be available in the incoming data for the fitting process.

  • Fit: fit(y ~ ., data): Includes a target feature that is a function of a "date" feature.

  • Predict: predict(model, new_data) where new_data contains a column named "date".

Date and Date-Time Variable

It's a requirement to have a date or date-time variable as a predictor. The fit() interface accepts date and date-time features and handles them internally.

See also

fit.model_spec(), set_engine()


if (FALSE) { library(tidymodels) library(modeltime.h2o) library(h2o) library(tidyverse) library(timetk) data_tbl <- walmart_sales_weekly %>% select(id, Date, Weekly_Sales) splits <- time_series_split( data_tbl, assess = "3 month", cumulative = TRUE ) recipe_spec <- recipe(Weekly_Sales ~ ., data = training(splits)) %>% step_timeseries_signature(Date) train_tbl <- bake(prep(recipe_spec), training(splits)) test_tbl <- bake(prep(recipe_spec), testing(splits)) # Initialize H2O h2o.init( nthreads = -1, ip = 'localhost', port = 54321 ) # ---- MODEL SPEC ---- model_spec <- automl_reg(mode = 'regression') %>% set_engine( engine = 'h2o', max_runtime_secs = 30, max_runtime_secs_per_model = 30, project_name = 'project_01', nfolds = 5, max_models = 1000, exclude_algos = c("DeepLearning"), seed = 786 ) model_spec # ---- TRAINING ---- # Important: Make sure the date is included as regressor. # This training process should take 30-40 seconds model_fitted <- model_spec %>% fit(Weekly_Sales ~ ., data = train_tbl) model_fitted # ---- PREDICT ---- # - IMPORTANT: New Data must have date feature predict(model_fitted, test_tbl) # Shutdown H2O when Finished. # Make sure to save any work before. h2o.shutdown(prompt = FALSE) }