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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.
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