Workflow Stages
In workflows: Modeling Workflows

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Workflows encompasses the three main stages of the modeling process: pre-processing of data, model fitting, and post-processing of results. This page enumerates the possible operations for each stage that have been implemented to date.

Pre-processing

There are three options for pre-processing but you can only use one of them in a single workflow:

A standard model formula via add_formula().
A tidyselect interface via add_variables() that strictly preserves the class of your columns.
A recipe object via add_recipe().

Model Fitting

parsnip model specifications are the only option here, specified via add_model().

When using a preprocessor, you may need an additional formula for special model terms (e.g. for mixed models or generalized linear models). In these cases, specify that formula using add_model()'s formula argument, which will be passed to the underlying model when fit() is called.

Post-processing

tailor post-processors are the only option here, specified via add_tailor(). Some examples of post-processing model predictions could include adding a probability threshold for two-class problems, calibration of probability estimates, truncating the possible range of predictions, and so on.