OgbonLab

Chapter 9 Quiz: Random Forest

Chapter 8: Tree-Based Models II — Random Forests

Learning objectives

Test your understanding of bagging, random forests, OOB error, and feature importance

This quiz covers both the lecture material and lab exercises from Chapter 9.

Key Concepts Review

Bagging: Train on bootstrap samples, aggregate via voting/averaging.
OOB: ~37% of data left out per tree. OOB error is a free validation estimate.
Feature Randomization: At each split, consider $\sqrt{p}$ random features (decorrelates trees).
Feature Importance: MDI (Gini-based) or permutation importance.
No Overfitting from More Trees: Performance plateaus but does not degrade.

References

Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.), ch. 15 (random forests). Springer.
James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). An Introduction to Statistical Learning (2nd ed.), ch. 8. Springer.
Bergen, K.J., Johnson, P.A., de Hoop, M.V., Beroza, G.C. (2019). Machine learning for data-driven discovery in solid Earth geoscience. Science 363, eaau0323.