NEWS.md
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).