Hello!
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 rsample
, recipes
, dials
and 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 parsnip
.
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 caret
, modelr
, yardstick
, rsample
, parsnip
, tensorflow
, keras
, cloudml
, and tfestimators
.