I knew the surface answer but fumbled explaining the sequential vs parallel distinction clearly.
Start by contrasting the core ensemble strategies: bagging (random forests) builds independent trees in parallel to reduce variance, while boosting builds trees sequentially to reduce bias. Then explain how these differences manifest in tree construction, weighting, and combination. Finally, discuss practical implications for performance and tuning.
Pro tip: Mention that random forests are easily parallelizable and robust to overfitting, while boosting requires careful tuning of learning rate and number of estimators to avoid overfitting, and is inherently sequential. This shows you understand both theory and engineering trade-offs.
Explain that random forests use bagging (bootstrap aggregating) to build multiple independent trees, while boosting builds trees sequentially, each correcting the errors of the previous ones.
In random forests, trees are deep and grown on bootstrap samples with random feature subsets; in boosting, trees are typically shallow (e.g., stumps) and trained on weighted data or gradients.
Random forests combine trees by averaging (regression) or majority voting (classification) with equal weights; boosting combines trees with weights based on their performance, often using a learning rate.
Random forests primarily reduce variance without increasing bias, while boosting reduces bias but can overfit if not properly regularized.
Mention that random forests are parallelizable and require less tuning, while boosting is sequential, sensitive to hyperparameters, and often achieves higher accuracy.
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