OgbonLab

Chapter 8 Quiz: Decision Trees

Chapter 7: Tree-Based Models I — Decision Trees

Learning objectives

Test your understanding of Gini impurity, entropy, information gain, and tree-based models

This quiz covers both the lecture material and lab exercises from Chapter 8.

Key Concepts Review

Gini: $G = 1 - \sum p_k^2$ . Pure: $G = 0$ . Max binary: $G = 0.5$ .
Entropy: $H = -\sum p_k \log_2 p_k$ . Pure: $H = 0$ . Max binary: $H = 1$ .
Information Gain: $IG = H_{parent} - \sum \frac{n_j}{n} H_{child_j}$ .
Pruning: Pre-pruning (max_depth, min_samples_leaf); post-pruning (ccp_alpha).
Pros: Interpretable, no scaling needed. Cons: Overfit-prone, unstable.

References

Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.), ch. 9. Springer.
James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). An Introduction to Statistical Learning (2nd ed.), ch. 8. Springer.
Murphy, K.P. (2022). Probabilistic Machine Learning: An Introduction, ch. 18. MIT Press.