Artificial intelligence · University
Overfitting, regularization and data distribution · revision notes
A model can fit training-specific noise or patterns that do not generalize. Compare training and appropriately held-out performance.
Key ideas and conditions
Overfitting
A model can fit training-specific noise or patterns that do not generalize. Compare training and appropriately held-out performance.
Regularization
Penalties or constraints can prefer simpler parameter solutions. Their effect depends on the model, scaling and chosen strength.
Bias-variance
Model flexibility, sample size and noise influence prediction error. The tradeoff is a framework, not a universal curve guaranteed in all modern settings.
Distribution shift
New data may differ from training data. Validation should reflect the intended use, and monitoring may reveal later drift.
Explain without looking
- Why is collecting more data not a guaranteed fix for incorrect labels or leakage?
- Training error is 0.05 and validation error is 0.12 in the same metric. Find validation minus training error.
Further reading and review references
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