This takes in a calibration tibble and computes the ranks of the models inside of it.
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()
Examples
# NOT RUN
if (FALSE) {
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)
}