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I think they mean "always (statistically) significantly larger". They're probably imagining that something like cross-validation would make test errors approxim

I think they mean "always (statistically) significantly larger". They're probably imagining that something like cross-validation would make test errors approximately equal to training errors, but if you consistently see significantly larger errors, then you've overfit.

bbstats 3y ago
When you find the minima of your validation curve, rarely if ever is your test loss lower than your training loss. I don't think this necessarily means you're overfitting.