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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.
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.
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.
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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.