Add `TestOnly` resampling strategy?

Sometimes you have trained a supervised machine on some data and you want to evaluate on some holdout set without retraining. Using evaluate!(..., resamping=Holdout()) doesn't allow this, so you would need to manually predict, and apply each metric to the prediction and test target, which is inconvenient, especially if you are tracking multiple metrics.

The idea of a TestOnly reampling strategy is that evaluate!(mach, resampling=TestOnly(), rows=test, measures=...) automates this: we assume mach is already trained (or throw an exception) and just evaluate the specified measures on predictions on the test rows.

Implementation looks pretty simple: train_test_pairs(::TestOnly, rows) = [(Int[], rows),] (i.e. empty train) and in evaluate! an empty train set will suppress training.

(It would be very convenient and natural that specifying no resampling strategy would fall back to TestOnly(), as in:

fit!(mach, rows=train)
evaluate!(mach, rows=test, measure=l2)

but that would be technically breaking - the current fallback is CV(). )

Start with the resampling entry point train_test_pairs and the evaluate! implementation, using the proposed TestOnly strategy and empty training rows as the scope. Check how trained machines, prediction rows, and measures are handled, then add coverage showing evaluation on rows=test without retraining and an exception for an untrained machine.