OgbonLab

Chapter 5 Quiz: Linear & Logistic Regression

Chapter 4: Linear Models — Regression and Classification

Learning objectives

Test your understanding of linear regression, logistic regression, and scikit-learn implementation

This quiz covers both the lecture material and lab exercises from Chapter 5.

Key Concepts Review

Linear Regression: $\hat{y} = \mathbf{w}^T\mathbf{x} + b$ . MSE cost. Normal equation: $\mathbf{w} = (X^TX)^{-1}X^Ty$ .
Logistic Regression: Sigmoid $\sigma(z) = \frac{1}{1+e^{-z}}$ . Binary cross-entropy loss.
Evaluation: MSE, MAE, $R^2 = 1 - SS_{res}/SS_{tot}$ . For classification: precision, recall, F1.
Regularization: Ridge (L2) shrinks weights; Lasso (L1) drives some to zero.

References

Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.), ch. 3 & 4. Springer.
James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). An Introduction to Statistical Learning (2nd ed.), ch. 3 & 4. Springer.
Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830.