sknifedatar (version 0.1.2)

modeltime_wfs_bestmodel: Modeltime best workflow from a set of models

Description

get best workflows generated from the modeltime_wfs_fit() function output.

Usage

modeltime_wfs_bestmodel(
  .wfs_results,
  .model = NULL,
  .metric = "rmse",
  .minimize = TRUE
)

Arguments

.wfs_results

a tibble generated from the modeltime_wfs_fit() function.

.model

string or number, It can be supplied as follows: <U+201C>top n,<U+201D> <U+201C>Top n<U+201D> or <U+201C>tOp n<U+201D>, where n is the number of best models to select; n, where n is the number of best models to select; name of the workflow or workflows to select.

.metric

metric to get best model from ('mae', 'mape','mase','smape','rmse','rsq')

.minimize

a boolean indicating whether to minimize (TRUE) or maximize (FALSE) the metric.

Value

a tibble containing the best model based on the selected metric.

Details

the best model is selected based on a specific metric ('mae', 'mape','mase','smape','rmse','rsq'). The default is to minimize the metric. However, if the model is being selected based on rsq minimize should be FALSE.

Examples

# NOT RUN {
library(dplyr)
library(earth)
data <- sknifedatar::data_avellaneda %>% mutate(date=as.Date(date)) %>% filter(date<'2012-06-01')

recipe_date <- recipes::recipe(value ~ ., data = data) %>% 
  recipes::step_date(date, features = c('dow','doy','week','month','year')) 

mars <- parsnip::mars(mode = 'regression') %>%
  parsnip::set_engine('earth')

wfsets <- workflowsets::workflow_set(
  preproc = list(
    R_date = recipe_date),
  models  = list(M_mars = mars),
  cross   = TRUE)

wffits <- sknifedatar::modeltime_wfs_fit(.wfsets = wfsets, 
                                         .split_prop = 0.8, 
                                         .serie=data)

sknifedatar::modeltime_wfs_bestmodel(.wfs_results = wffits,
                                     .metric='rsq',
                                     .minimize = FALSE)
                                  
# }