Workflows encompasses the three main stages of the modelingprocess: pre-processing of data, model fitting, andpost-processing of results. This page enumerates the possible operationsfor each stage that have been implemented to date.
Pre-processing
The two elements allowed for pre-processing are:
A standard modelformula 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 forspecial model terms (e.g.for mixed models or generalized linearmodels). In these cases, specify that formula usingadd_model()’s formula argument, which will bepassed 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 ofprobability estimates, truncating the possible range of predictions, andso on.
None of these are currently implemented but will be in comingversions.
