View source: R/wfs-linear-reg.R
ts_wfs_lin_reg | R Documentation |
This function is used to quickly create a workflowsets object.
ts_wfs_lin_reg(.model_type, .recipe_list, .penalty = 1, .mixture = 0.5)
.model_type |
This is where you will set your engine. It uses
Not yet implemented are:
|
.recipe_list |
You must supply a list of recipes. list(rec_1, rec_2, ...) |
.penalty |
The penalty parameter of the glmnet. The default is 1 |
.mixture |
The mixture parameter of the glmnet. The default is 0.5 |
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 glmnet
model specification, but if you choose you can
set them yourself if you have a good understanding of what they should be.
Returns a workflowsets object.
Steven P. Sanderson II, MPH
https://workflowsets.tidymodels.org/(workflowsets)
Other Auto Workflowsets:
ts_wfs_arima_boost()
,
ts_wfs_auto_arima()
,
ts_wfs_ets_reg()
,
ts_wfs_mars()
,
ts_wfs_nnetar_reg()
,
ts_wfs_prophet_reg()
,
ts_wfs_svm_poly()
,
ts_wfs_svm_rbf()
,
ts_wfs_xgboost()
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_lin_reg("all_engines", rec_objs)
wf_sets
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