I'm looking for another reference guide other than this article A gentle intro to tidymodels which would explain in more detail the process of using
parsnip for a fairly complicated ML project where I'd like to, e.g.: apply a number of preprocessing steps with
recipes on a nested, re-sampled dataset with
rsample in order to hypertune and find the best model parameters with
dials and eventually fit the best model with
Is there any guideline that would discuss this A to Z? There's a lot of good articles on individual packages websites but I've already read them all and they never really show the full picture. I'd like already to start using all of these packages together instead of reverting back to
caret to do the final modelling piece.
For discussions related to modeling, machine learning and deep learning. Related packages include