> Or would you cache the return of "fit" as well as "transform"?
Caching fit rather than transform. Fit is usually the costly step. > Caching "fit" with joblib seems non-trivial. Why? Caching a function that takes the estimator and X and y should do it. The transformer would clone the estimator on fit, to avoid side-effects that would trigger recomputes. It's a pattern that I use often, I've just never coded a good transformer for it. On my usecases, it works very well, provided that everything is nicely seeded. Also, the persistence across sessions is a real time saver. _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
