modeltime_wfs_multirefit: Refit one or more trained workflows to new data

In sknifedatar: Swiss Knife of Data

Description

It allows retraining a set of workflows trained on new data.

Usage

modeltime_wfs_multirefit(models_table)

Arguments

models_table

a tibble that comes from the output of the modeltime_wfs_multifit(), modeltime_wfs_multiforecast(), modeltime_wfs_multibestmodel() functions. For the modeltime_wfs_multifit function, the 'table_time' object must be selected from the output.

Value

a tibble, corresponds to the same tibble supplied in the 'models_table' parameter but with the refit of the workflows saved in the 'nested_model' column.

Examples

library(dplyr)
library(earth)

df <- sknifedatar::emae_series

datex <- '2020-02-01'
df_emae <- df %>% 
  dplyr::filter(date <= datex) %>% 
  tidyr::nest(nested_column=-sector) %>% 
  head(2)

receta_base <- recipes::recipe(value ~ ., data = df %>% select(-sector))

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

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

wfsets_fit <- modeltime_wfs_multifit(.wfs = wfsets,
                                     .prop = 0.8, 
                                     serie = df_emae)

sknifedatar::modeltime_wfs_multirefit(wfsets_fit$table_time)

sknifedatar documentation built on June 1, 2021, 9:08 a.m.