modeltime_wfs_rank: Modeltime workflow sets ranking based on a metric

Description

generates a ranking of models generated with modeltime_wfs_fit() function.

Usage

modeltime_wfs_rank(.wfs_results, rank_metric = NULL, minimize = TRUE)

Arguments

.wfs_results

a tibble generated with the modeltime_wfs_fit() function.

rank_metric

the metric used to generate the ranking 'mae', 'mape','mase','smape','rmse','rsq'.

minimize

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

Value

a tibble containing the models ranked by a specific metric.

Details

the ranking depends on the metric selected.

Examples

Run this code

# NOT RUN {
library(dplyr)
library(modeltime)
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_rank(.wfs_results = wffits,
                                rank_metric = 'rsq',
                                minimize = FALSE)
                                
# }