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")
mode | 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)
h2o
The engine uses h2o.automl()
.
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.
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) }