Choose Between Random Forests and Gradient Boosting Models
Company: TikTok
Role: Data Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
##### Scenario
Product-facing data-science interview on choosing and configuring tree-based ensemble models.
##### Question
Compare Random Forests with Gradient Boosting Decision Trees such as XGBoost. When would you prefer one over the other in a production setting? Do tree-based models require feature scaling or normalization? Explain the theoretical reason and any practical exceptions.
##### Hints
Discuss bias-variance, overfitting control, interpretability, parallelism, sequential boosting, split criteria independence from feature scale.
Quick Answer: This question evaluates understanding of tree-based ensemble methods and the competency to compare Random Forests versus Gradient Boosted Decision Trees across learning dynamics, bias–variance trade-offs, overfitting control, interpretability, training and inference parallelism, and feature preprocessing considerations.