Auto MARS (Earth) Workflowset Function

This function is used to quickly create a workflowsets object.

Usage

ts_wfs_mars(
  .model_type = "earth",
  .recipe_list,
  .num_terms = 200,
  .prod_degree = 1,
  .prune_method = "backward"
)

Arguments

.model_type

This is where you will set your engine. It uses parsnip::mars() under the hood and can take one of the following:

• "earth"

.recipe_list

You must supply a list of recipes. list(rec_1, rec_2, ...)

.num_terms

The number of features that will be retained in the final model, including the intercept.

.prod_degree

The highest possible interaction degree.

.prune_method

The pruning method. This is a character, the default is "backward". You can choose from one of the following:

• "backward"

• "none"

• "exhaustive"

• "forward"

• "seqrep"

• "cv"

Value

Returns a workflowsets object.

Details

This function expects to take in the recipes that you want to use in the modeling process. This is an automated workflow process. There are sensible defaults set for the model specification, but if you choose you can set them yourself if you have a good understanding of what they should be. The mode is set to "regression".

This only uses the option set_engine("earth") and therefore the .model_type is not needed. The parameter is kept because it is possible in the future that this could change, and it keeps with the framework of how other functions are written.

See also

https://workflowsets.tidymodels.org/

https://parsnip.tidymodels.org/reference/mars.html

Other Auto Workflowsets: ts_wfs_arima_boost(), ts_wfs_auto_arima(), ts_wfs_ets_reg(), ts_wfs_lin_reg(), ts_wfs_nnetar_reg(), ts_wfs_prophet_reg(), ts_wfs_svm_poly(), ts_wfs_svm_rbf(), ts_wfs_xgboost()

Author

Steven P. Sanderson II, MPH

Examples

suppressPackageStartupMessages(library(modeltime))
suppressPackageStartupMessages(library(timetk))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(rsample))

data <- AirPassengers %>%
  ts_to_tbl() %>%
  select(-index)

splits <- time_series_split(
   data
  , date_col
  , assess = 12
  , skip = 3
  , cumulative = TRUE
)

rec_objs <- ts_auto_recipe(
 .data = training(splits)
 , .date_col = date_col
 , .pred_col = value
)

wf_sets <- ts_wfs_mars("earth", rec_objs)
wf_sets
#> # A workflow set/tibble: 4 × 4
#>   wflow_id                  info             option    result    
#>   <chr>                     <list>           <list>    <list>    
#> 1 rec_base_mars             <tibble [1 × 4]> <opts[0]> <list [0]>
#> 2 rec_date_mars             <tibble [1 × 4]> <opts[0]> <list [0]>
#> 3 rec_date_fourier_mars     <tibble [1 × 4]> <opts[0]> <list [0]>
#> 4 rec_date_fourier_nzv_mars <tibble [1 × 4]> <opts[0]> <list [0]>