Investigate best tuning parameters — show_best.tune

tune::show_best() displays the top sub-models and their performance estimates.

Usage

# S3 method for class 'tune_race'
show_best(
  x,
  ...,
  metric = NULL,
  eval_time = NULL,
  n = 5,
  call = rlang::current_env()
)

Arguments

x: The results of tune_grid() or tune_bayes().
...: For select_by_one_std_err() and select_by_pct_loss(), this argument is passed directly to dplyr::arrange() so that the user can sort the models from most simple to most complex. That is, for a parameter p, pass the unquoted expression p if smaller values of p indicate a simpler model, or desc(p) if larger values indicate a simpler model. At least one term is required for these two functions. See the examples below.
metric: A character value for the metric that will be used to sort the models. (See https://yardstick.tidymodels.org/articles/metric-types.html for more details). Not required if a single metric exists in x. If there are multiple metric and none are given, the first in the metric set is used (and a warning is issued).
eval_time: A single numeric time point where dynamic event time metrics should be chosen (e.g., the time-dependent ROC curve, etc). The values should be consistent with the values used to create x. The NULL default will automatically use the first evaluation time used by x.
n: An integer for the maximum number of top results/rows to return.
call: The call to be shown in errors and warnings.

Details

For racing results (from the finetune package), it is best to only report configurations that finished the race (i.e., were completely resampled). Comparing performance metrics for configurations averaged with different resamples is likely to lead to inappropriate results.