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

The two elements allowed for pre-processing are:

A standard model formula via

`add_formula()`

.A recipe object via

`add_recipe()`

.

You can use one or the other but not both.

## 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

Some examples of post-processing the model predictions would be: adding a probability threshold for two-class problems, calibration of probability estimates, truncating the possible range of predictions, and so on.

None of these are currently implemented but will be in coming versions.