Choose Between Random Forests and Gradient Boosting Models

Q: Choose Between Random Forests and Gradient Boosting Models

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.

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Question

Scenario

Product-facing data science interview on choosing and configuring tree-based ensemble models for tabular prediction in a production setting.

Question

Compare Random Forests (RF) with Gradient Boosted Decision Trees (GBDT), such as XGBoost.

What are the key differences in how they learn and generalize (bias–variance, overfitting control, interpretability, training/inference parallelism)?
In production, when would you prefer one over the other?
Do tree-based models require feature scaling or normalization? Explain the theoretical reason and any practical exceptions.

Hints

Bias–variance trade-off, robustness to noise
Overfitting control: bagging vs sequential boosting and regularization
Interpretability options and stability
Parallelism: independent trees vs sequential boosting, GPU/CPU considerations
Split criteria and invariance to feature scale

Choose Between Random Forests and Gradient Boosting Models

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