Cross-Validation in Machine Learning: How to Do It Right - neptune.ai

In machine learning (ML), generalization usually refers to the ability of an algorithm to be effective across various inputs. It means that the ML model does not encounter performance degradation on the new inputs from the same distribution of the training data. For human beings generalization is the most natural thing possible. We can classify on the fly. For example, we would definitely recognize a dog even if we didn't see this breed before. Nevertheless, it might be quite a challenge for an ML model.