rank_results | R Documentation |
This function sorts the results by a specific performance metric.
rank_results(x, rank_metric = NULL, eval_time = NULL, select_best = FALSE)
x |
A |
rank_metric |
A character string for a metric. |
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 |
select_best |
A logical giving whether the results should only contain the numerically best submodel per workflow. |
If some models have the exact same performance,
rank(value, ties.method = "random")
is used (with a reproducible seed) so
that all ranks are integers.
No columns are returned for the tuning parameters since they are likely to
be different (or not exist) for some models. The wflow_id
and .config
columns can be used to determine the corresponding parameter values.
A tibble with columns: wflow_id
, .config
, .metric
, mean
,
std_err
, n
, preprocessor
, model
, and rank
.
The package supplies two pre-generated workflow sets, two_class_set
and chi_features_set
, and associated sets of model fits
two_class_res
and chi_features_res
.
The two_class_*
objects are based on a binary classification problem
using the two_class_dat
data from the modeldata package. The six
models utilize either a bare formula or a basic recipe utilizing
recipes::step_YeoJohnson()
as a preprocessor, and a decision tree,
logistic regression, or MARS model specification. See ?two_class_set
for source code.
The chi_features_*
objects are based on a regression problem using the
Chicago
data from the modeldata package. Each of the three models
utilize a linear regression model specification, with three different
recipes of varying complexity. The objects are meant to approximate the
sequence of models built in Section 1.3 of Kuhn and Johnson (2019). See
?chi_features_set
for source code.
chi_features_res
rank_results(chi_features_res)
rank_results(chi_features_res, select_best = TRUE)
rank_results(chi_features_res, rank_metric = "rsq")
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