principles

I didn't have enough patience to wait for Max to make a tidymodels/principles repo, so I made this outline and filled in the smattering of notes I have.

Things I think we need to understand and treat as separate throughout:

• programmatic versus interactive modelling
• end goal of inference versus end goal of prediction
• a single fit versus a set of fits

Other things to do:

• decide on some canonical modelling examples. proposals:
  • LASSO for a model specification with structure hyperparameter space
  • random forest for a model specification with unstructured hyperparameter space

Another potential topic: wrapping and working with existing modelling packages