##### Scenario Product-facing data-science interview on choosing and configuring tree-based ensemble models. The team wants to understand the trade-offs between Random Forests and Gradient-Boosted Decision Trees and whether any feature scaling is required for tree-based algorithms. ##### Question Compare Random Forests with Gradient-Boosted Decision Trees such as XGBoost. Specifically: 1. Contrast them on **bias/variance**, **interpretability**, **training and inference speed**, and **robustness to overfitting**, explaining how ensemble construction (bagging vs. sequential boosting) drives each difference. 2. **When would you prefer one over the other in a production setting?** Consider accuracy ceiling, tuning effort, latency/throughput, robustness to noise, calibration, and distribution drift. 3. Do tree-based models require **feature standardization or normalization**? Explain the theoretical reason and any practical exceptions. ##### Hints Focus on ensemble construction, sequential vs. parallel learning, split criteria, overfitting control knobs, and why splits are invariant to monotonic transformations of the features.