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Auto XGBoost (XGBoost) Workflowset Function

Source: R/wfs-xgboost-reg.R
ts_wfs_xgboost.Rd

This function is used to quickly create a workflowsets object.

Usage

ts_wfs_xgboost(
  .model_type = "xgboost",
  .recipe_list,
  .trees = 15L,
  .min_n = 1L,
  .tree_depth = 6L,
  .learn_rate = 0.3,
  .loss_reduction = 0,
  .sample_size = 1,
  .stop_iter = Inf
)

Arguments

.model_type

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

  • "xgboost"

.recipe_list

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

.trees

The number of trees (type: integer, default: 15L)

.min_n

Minimal Node Size (type: integer, default: 1L)

.tree_depth

Tree Depth (type: integer, default: 6L)

.learn_rate

Learning Rate (type: double, default: 0.3)

.loss_reduction

Minimum Loss Reduction (type: double, default: 0.0)

.sample_size

Proportion Observations Sampled (type: double, default: 1.0)

.stop_iter

The number of ierations Before Stopping (type: integer, default: Inf)

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("xgboost") 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.

parsnip::boost_tree() xgboost::xgb.train() creates a series of decision trees forming an ensemble. Each tree depends on the results of previous trees. All trees in the ensemble are combined to produce a final prediction.

See also

https://workflowsets.tidymodels.org/

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

https://arxiv.org/abs/1603.02754

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

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_xgboost("xgboost", rec_objs)
wf_sets
#> # A workflow set/tibble: 4 × 4
#>   wflow_id                        info             option    result    
#>   <chr>                           <list>           <list>    <list>    
#> 1 rec_base_boost_tree             <tibble [1 × 4]> <opts[0]> <list [0]>
#> 2 rec_date_boost_tree             <tibble [1 × 4]> <opts[0]> <list [0]>
#> 3 rec_date_fourier_boost_tree     <tibble [1 × 4]> <opts[0]> <list [0]>
#> 4 rec_date_fourier_nzv_boost_tree <tibble [1 × 4]> <opts[0]> <list [0]>