Generalization in statistical learning

Generalization is the ability of a fitted prediction rule to perform well on new observations under a specified target probability distribution. It is assessed through prediction error rather than training error. The target distribution and use of data are part of the question: dataset shift can change performance, and data leakage can make an apparent assessment misleading.