fit_resamples()
computes a set of performance metrics across one or more
resamples. It does not perform any tuning (see tune_grid()
and
tune_bayes()
for that), and is instead used for fitting a single
model+recipe or model+formula combination across many resamples.
fit_resamples(object, ...) # S3 method for model_spec fit_resamples( object, preprocessor, resamples, ..., metrics = NULL, control = control_resamples() ) # S3 method for workflow fit_resamples( object, resamples, ..., metrics = NULL, control = control_resamples() )
object  A 

...  Currently unused. 
preprocessor  A traditional model formula or a recipe created using

resamples  A resample 
metrics  A 
control  A 
To use your own performance metrics, the yardstick::metric_set()
function
can be used to pick what should be measured for each model. If multiple
metrics are desired, they can be bundled. For example, to estimate the area
under the ROC curve as well as the sensitivity and specificity (under the
typical probability cutoff of 0.50), the metrics
argument could be given:
metrics = metric_set(roc_auc, sens, spec)
Each metric is calculated for each candidate model.
If no metric set is provided, one is created:
For regression models, the root mean squared error and coefficient of determination are computed.
For classification, the area under the ROC curve and overall accuracy are computed.
Note that the metrics also determine what type of predictions are estimated during tuning. For example, in a classification problem, if metrics are used that are all associated with hard class predictions, the classification probabilities are not created.
The outofsample estimates of these metrics are contained in a list column
called .metrics
. This tibble contains a row for each metric and columns
for the value, the estimator type, and so on.
collect_metrics()
can be used for these objects to collapse the results
over the resampled (to obtain the final resampling estimates per tuning
parameter combination).
When control(save_preds = TRUE)
, the output tibble contains a list column
called .predictions
that has the outofsample predictions for each
parameter combination in the grid and each fold (which can be very large).
The elements of the tibble are tibbles with columns for the tuning
parameters, the row number from the original data object (.row
), the
outcome data (with the same name(s) of the original data), and any columns
created by the predictions. For example, for simple regression problems, this
function generates a column called .pred
and so on. As noted above, the
prediction columns that are returned are determined by the type of metric(s)
requested.
This list column can be unnested
using tidyr::unnest()
or using the
convenience function collect_predictions()
.
The extract
control option will result in an additional function to be
returned called .extracts
. This is a list column that has tibbles
containing the results of the user's function for each tuning parameter
combination. This can enable returning each model and/or recipe object that
is created during resampling. Note that this could result in a large return
object, depending on what is returned.
The control function contains an option (extract
) that can be used to
retain any model or recipe that was created within the resamples. This
argument should be a function with a single argument. The value of the
argument that is given to the function in each resample is a workflow
object (see workflows::workflow()
for more information). There are two
helper functions that can be used to easily pull out the recipe (if any)
and/or the model: extract_recipe()
and extract_model()
.
As an example, if there is interest in getting each model back, one could use:
extract = function (x) extract_model(x)
Note that the function given to the extract
argument is evaluated on
every model that is fit (as opposed to every model that is evaluated).
As noted above, in some cases, model predictions can be derived for
submodels so that, in these cases, not every row in the tuning parameter
grid has a separate R object associated with it.
# \donttest{ library(recipes) library(rsample) library(parsnip) set.seed(6735) folds < vfold_cv(mtcars, v = 5) spline_rec < recipe(mpg ~ ., data = mtcars) %>% step_ns(disp) %>% step_ns(wt) lin_mod < linear_reg() %>% set_engine("lm") control < control_resamples(save_pred = TRUE) spline_res < fit_resamples(lin_mod, spline_rec, folds, control = control) spline_res#> # Resampling results #> # 5fold crossvalidation #> # A tibble: 5 x 5 #> splits id .metrics .notes .predictions #> <list> <chr> <list> <list> <list> #> 1 <split [25/7]> Fold1 <tibble [2 × 3]> <tibble [0 × 1]> <tibble [7 × 3]> #> 2 <split [25/7]> Fold2 <tibble [2 × 3]> <tibble [0 × 1]> <tibble [7 × 3]> #> 3 <split [26/6]> Fold3 <tibble [2 × 3]> <tibble [0 × 1]> <tibble [6 × 3]> #> 4 <split [26/6]> Fold4 <tibble [2 × 3]> <tibble [0 × 1]> <tibble [6 × 3]> #> 5 <split [26/6]> Fold5 <tibble [2 × 3]> <tibble [0 × 1]> <tibble [6 × 3]>#> # A tibble: 1 x 5 #> .metric .estimator mean n std_err #> <chr> <chr> <dbl> <int> <dbl> #> 1 rmse standard 3.11 5 0.168# }