How are the findings learnt from the data set are generalized compared to Statistics?
Bias-Variance decomposition is one way, and V-C dimension/bound is another...

Both of these are metrics you can use to get a feeling of how confident you should be that your training results will generalize to out-of-sample.

V-C dimension focuses on the results of this learning algorithm outcome.
Bias-Variance focuses on the expected outcome of the algorithm itself.

Pick your poison - I sincerely hope that this was helpful.