I Kept Doing the Same Sanity Check on Every Small Dataset, So I Turned It Into a Package

After writing up two small projects - one predicting data science salaries, one checking whether Punxsutawney Phil's shadow call means anything - I noticed I'd done the same manual step both times: train a couple of models, check the test-set R², then immediately re-run everything with cross-validation before trusting that number. On a dataset with a few hundred rows, a single train/test split can make a weaker model look like the winner purely by chance, and I didn't want to keep catching that…