fit will take a model specification, finalize the required
code by substituting arguments, and execute the model fit
routine.
fit(object, ...)# S3 method for model_spec
fit(object, x, engine = object$engine,
.control = list(verbosity = 1, catch = FALSE), ...)
An object of class model_spec
Other options required to fit the model. If x is a
formula or recipe, then the data argument should be passed
here. For the "x/y" interface, the outcome data should be passed
in with the argument y.
Either an R formula, a data frame of predictors, or a recipe object.
A character string for the software that should be used to fit the model. This is highly dependent on the type of model (e.g. linear regression, random forest, etc.).
A named list with elements verbosity and
catch. verbosity should be an integer where a value of zero
indicates that no messages or output should be shown when
packages are loaded or when the model is fit. A value of 1 means
that package loading is quiet but model fits can produce output
to the screen (depending on if they contain their own
verbose-type argument). A value of 2 or more indicates that
any output should be seen. catch is a logical where a value of
TRUE will evaluate the model inside of try(, silent = TRUE).
If the model fails, an object is still returned (without an
error) that inherits the class "try-error".
An object for the fitted model.
fit substitutes the current arguments in the model
specification into the computational engine's code, checks them
for validity, then fits the model using the data and the
engine-specific code. Different model functions have different
interfaces (e.g. formula or x/y) and fit translates
between the interface used when fit was invoked and the one
required by the underlying model.
When possible, fit attempts to avoid making copies of the
data. For example, if the underlying model uses a formula and
fit is invoked with a formula, the original data are references
when the model is fit. However, if the underlying model uses
something else, such as x/y, the formula is evaluated and
the data are converted to the required format. In this case, any
calls in the resulting model objects reference the temporary
objects used to fit the model.