add_variables() now allows for specifying a bundle of model terms through add_variables(variables = ), supplying a pre-created set of variables with the new workflow_variables() helper. This is useful for supplying a set of variables programmatically (#92).
New is_trained_workflow() for determining if a workflow has already been trained through a call to fit() (#91).
fit() now errors immediately if control is not created by control_workflow() (#89).
Added broom::augment() and broom::glance() methods for trained workflow objects (#76).
Added support for butchering a workflow using butcher::butcher().
Updated to testthat 3.0.0.
.fit_finalize() for internal usage by the tune package.New add_variables() for specifying model terms using tidyselect expressions with no extra preprocessing. For example:
wf <- workflow() %>%
add_variables(y, c(var1, start_with("x_"))) %>%
add_model(spec_lm)One benefit of specifying terms in this way over the formula method is to avoid preprocessing from model.matrix(), which might strip the class of your predictor columns (as it does with Date columns) (#34).
add_formula(), workflows now uses model-specific information from parsnip to decide whether to expand factors via dummy encoding (n - 1 levels), one-hot encoding (n levels), or no expansion at all. This should result in more intuitive behavior when working with models that don’t require dummy variables. For example, if a parsnip rand_forest() model is used with a ranger engine, dummy variables will not be created, because ranger can handle factors directly (#51, #53).NEWS.md file to track changes to the package.