Fit and predict from tailors

These functions apply fit() and predict() methods for each adjustment added to a tailor, in the order in which they were applied.

Users do not need to interface with these methods directly when tailors are situated inside model workflows with ?workflows::add_tailor().

Usage

# S3 method for class 'tailor'
fit(object, .data, outcome, estimate, probabilities = c(), ...)

# S3 method for class 'tailor'
predict(object, new_data, ...)

Arguments

object

A tailor().

.data, new_data

A data frame containing predictions from a model.

outcome

<tidy-select> Only required when used independently of ?workflows::add_tailor(), and can also be passed at fit() time instead. The column name of the outcome variable.

estimate

<tidy-select> Only required when used independently of ?workflows::add_tailor(), and can also be passed at fit() time instead. The column name of the point estimate (e.g. predicted class), In tidymodels, this corresponds to column names .pred, .pred_class, or .pred_time.

probabilities

<tidy-select> Only required when used independently of ?workflows::add_tailor() for the "binary" or "multiclass" types, and can also be passed at fit() time instead. The column names of class probability estimates. These should be given in the order of the factor levels of the estimate.

...

Currently ignored.

Data Usage

For adjustments that don't require estimating parameters, training with fit() simply evaluates tidyselect expressions and logs column names. For others, as in adjust_numeric_calibration(), adjustments actually learn from data; in that case, separate subsets of data ought to be used for training the tailor and evaluating its performance on predictions. See the Data Usage section in ?workflows::add_tailor() for more information on how tidymodels makes that split; when situated in a model workflow, tailors will automatically be trained on the appropriate subset of data.