Compare decision trees and random forests

Contrast decision trees and random forests.

Your answer should address:

• The construction of a single decision tree, along with its key strengths and weaknesses.
• The way a random forest is built from many trees, including bagging and feature subsampling.
• How the two approaches compare in terms of bias, variance, tendency to overfit, interpretability, and common applications.

Overview: This prompt tests understanding of supervised learning models, ensemble techniques, and the bias–variance trade-off by directly comparing decision trees and random forests. It is frequently used to explore model-selection trade-offs such as overfitting versus interpretability, sits within Machine Learning, and checks both conceptual knowledge and practical judgment.