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Generalization in Classification
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We discusses the importance of generalization in classification, where the goal is to train a model that can accurately predict labels for previously unseen data. The text first explores the role of test sets in evaluating model performance, emphasizing the need to use them sparingly and cautiously to avoid overfitting. It then introduces the concept of statistical learning theory, which aims to provide theoretical guarantees for model generalization by bounding the difference between a model's training error and its true error on the underlying population. The text highlights the use of the Vapnik–Chervonenkis (VC) dimension as a measure of model complexity, but acknowledges its limitations in explaining the generalization capabilities of deep neural networks. Finally, the text previews the upcoming discussion on generalization in the context of deep learning, suggesting that alternative explanations may be needed to understand the impressive performance of these complex models.
Read more here: https://d2l.ai/chapter_linear-classification/generalization-classification.html
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