Model Rank

This takes in a calibration tibble and computes the ranks of the models inside of it.

Usage

ts_model_rank_tbl(.calibration_tbl)

Arguments

.calibration_tbl

A calibrated modeltime table.

Value

A tibble with models ranked by metric performance order

Details

This takes in a calibration tibble and computes the ranks of the models inside of it. It computes for now only the default yardstick metrics from modeltime These are the following using the dplyr min_rank() function with desc use on rsq:

• "rmse"

• "mae"

• "mape"

• "smape"

• "rsq"

See also

Other Utility: auto_stationarize(), calibrate_and_plot(), internal_ts_backward_event_tbl(), internal_ts_both_event_tbl(), internal_ts_forward_event_tbl(), model_extraction_helper(), ts_get_date_columns(), ts_info_tbl(), ts_is_date_class(), ts_lag_correlation(), ts_model_auto_tune(), ts_model_compare(), ts_model_spec_tune_template(), ts_qq_plot(), ts_scedacity_scatter_plot(), ts_to_tbl(), util_difflog_ts(), util_doublediff_ts(), util_doubledifflog_ts(), util_log_ts(), util_singlediff_ts()

Author

Steven P. Sanderson II, MPH

Examples

# NOT RUN
if (FALSE) { # \dontrun{
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(timetk))
suppressPackageStartupMessages(library(modeltime))
suppressPackageStartupMessages(library(rsample))
suppressPackageStartupMessages(library(workflows))
suppressPackageStartupMessages(library(parsnip))
suppressPackageStartupMessages(library(recipes))

data_tbl <- ts_to_tbl(AirPassengers) %>%
  select(-index)

splits <- time_series_split(
  data_tbl,
  date_var = date_col,
  assess = "12 months",
  cumulative = TRUE
)

rec_obj <- recipe(value ~ ., training(splits))

model_spec_arima <- arima_reg() %>%
  set_engine(engine = "auto_arima")

model_spec_mars <- mars(mode = "regression") %>%
  set_engine("earth")

wflw_fit_arima <- workflow() %>%
  add_recipe(rec_obj) %>%
  add_model(model_spec_arima) %>%
  fit(training(splits))

wflw_fit_mars <- workflow() %>%
  add_recipe(rec_obj) %>%
  add_model(model_spec_mars) %>%
  fit(training(splits))

model_tbl <- modeltime_table(wflw_fit_arima, wflw_fit_mars)

calibration_tbl <- model_tbl %>%
  modeltime_calibrate(new_data = testing(splits))

ts_model_rank_tbl(calibration_tbl)

} # }