Chapter 11 Quiz: Overfitting & Bias-Variance Tradeoff
Learning objectives
- Test your understanding of bias-variance tradeoff, regularization, and cross-validation
This quiz covers the lecture material from Chapter 11, focusing on model generalization.
Key Concepts Review
- Bias-Variance: Total Error = Bias + Variance + Noise. Complex models: low bias, high variance.
- Overfitting: Low train error, high test error. Fix: regularization, more data, simpler model.
- Underfitting: High error on both sets. Fix: more features, more complex model.
- Cross-Validation: k-fold (k=5 or 10) balances bias and variance of the estimate.
- Regularization: L1 (sparsity), L2 (shrinkage), dropout, early stopping.
References
- Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.), ch. 7 & 3. Springer.
- James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). An Introduction to Statistical Learning (2nd ed.), ch. 5 & 6. Springer.
- Bishop, C.M. (2006). Pattern Recognition and Machine Learning, ch. 1.5 & 3.1. Springer.