Started with the textbook definitions and train vs.
Define overfitting and underfitting clearly, then contrast their causes, detection methods, and remedies in a structured way. Use a concrete example or two to illustrate, and emphasize the bias-variance trade-off and practical steps like regularization and cross-validation. Show awareness of Apple's focus on on-device ML and privacy by mentioning data efficiency and model size constraints.
Pro tip: Mention that in production, especially on-device, you often prefer slight underfitting over overfitting because overfit models fail unpredictably on new user data, and you can't easily retrain on-device. This shows you think about real-world deployment trade-offs, not just theory.
Briefly define overfitting (model learns noise, high variance) and underfitting (model too simple, high bias). Mention they are ends of the bias-variance spectrum.
For overfitting: complex models, small datasets, noisy features, too many parameters. For underfitting: overly simple models, insufficient features, excessive regularization.
Use learning curves, validation curves, and metrics like training vs validation error. Overfitting: low training error, high validation error. Underfitting: both errors high and close.
For overfitting: more data, data augmentation, regularization (L1/L2, dropout), early stopping, simpler models, cross-validation. For underfitting: more complex models, better features, reduce regularization, train longer.
Relate to Apple's context: on-device constraints, privacy-preserving data, and the need for robust generalization. Mention that fixing requires iterative experimentation.
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