Description: Generalization error refers to the discrepancy between a machine learning model’s performance on training data and its performance on unseen data. In simple terms, it is the difference between the expected prediction error on the training set and the expected prediction error on a dataset that the model has not encountered before. This concept is crucial in the field of machine learning, as a model that fits too closely to the training data may have low error on that data but high error on new data, indicating that it has not generalized well. Generalization is essential for a model to be useful in real-world applications, where it is expected to make accurate predictions on data it has not seen before. A good model must balance complexity and generalization ability, avoiding both overfitting and underfitting. The evaluation of generalization error is commonly performed through techniques such as cross-validation, which allows estimating how the model will perform on unseen data by splitting the dataset into multiple subsets and training and evaluating the model on different combinations of these. In summary, generalization error is a key indicator of a machine learning model’s effectiveness and its ability to make accurate predictions in various real-world situations.
