Compare decision trees and random forests
Company: Amazon
Role: Applied Scientist
Category: Machine Learning
Difficulty: easy
Interview Round: Technical Screen
Compare **decision trees** and **random forests**.
In your answer, discuss:
- How a single decision tree is built and its main advantages and disadvantages.
- How a random forest is constructed from multiple trees (bagging and feature subsampling).
- Differences in bias, variance, overfitting behavior, interpretability, and typical use cases.
Overview: This question evaluates understanding of supervised learning models, ensemble methods, and bias–variance trade-offs specifically comparing decision trees and random forests. It is commonly asked to probe model selection trade-offs such as overfitting versus interpretability, falls under the Machine Learning category, and tests both conceptual understanding and practical application.
