← Machine Learning Foundations
Pro topic
Regularization & Generalization
Bias-variance, early stopping, and capacity control.
Regularization & Generalization
Overfitting means low training error but poor unseen performance.
Levers
- More / cleaner data
- Simpler hypothesis class
- L2 / weight decay, dropout, early stopping
- Data augmentation (when valid)
Bias–variance intuition
High bias: model too simple. High variance: model too sensitive to sample noise. Regularization trades a bit of bias for lower variance.
Continue this theory with Pro
You've reached the free preview. Pro unlocks the full write-up, videos, quiz scoring, and cheatsheet.