Explain random forests, bagging, and evaluation
Company: Amazon
Role: Data Scientist
Category: Machine Learning
Difficulty: hard
Interview Round: Onsite
Overview: This question evaluates understanding of ensemble learning and model evaluation, covering Random Forest aggregation, feature subsampling, bagging versus boosting, hyperparameter effects, out-of-bag (OOB) error, class-imbalance handling, feature-importance interpretation, and validation strategies for supervised tabular data in Machine Learning and Data Science. It is commonly asked to assess a candidate's reasoning about bias–variance trade-offs, robustness and evaluation choices for production-ready models; the domain is ensemble methods and model evaluation and the required level combines conceptual understanding with practical application.
Read the full Amazon Data Scientist interview experience this question came from
