Generalization error is the expected difference between a model’s performance on training data and its performance on new, unseen data. It measures how well a supervised learning model is likely to perform outside the data used to fit it.
For supervised learning applications in machine learning and statistical learning theory, generalization error (also known as the out-of-sample error or the risk) is a measure of how accurately a learning algorithm is able to predict outcomes for previously unseen data.