I knew this cold in theory but started rambling about bagging before actually defining what an ensemble is, which felt backwards in hindsight.
Start by defining random forests as an ensemble of decision trees, then explain the core mechanisms of bagging and feature randomness. Contrast with single models by highlighting variance reduction, robustness, and the bias-variance trade-off. Use a concrete example to illustrate the difference.
Pro tip: Mention that random forests are embarrassingly parallel and discuss how they handle missing values and feature importance, showing practical ML engineering awareness. Also, note that while they are strong baselines, they can be outperformed by gradient boosting in some cases, demonstrating trade-off thinking.
Explain that a random forest is an ensemble of decision trees, typically trained via bagging (bootstrap aggregating) and random feature selection at each split.
Describe the training process: bootstrap samples, random subset of features for each split, and aggregation of predictions (majority vote for classification, average for regression).
Discuss how a single decision tree is prone to overfitting and high variance, while random forests reduce variance by averaging multiple decorrelated trees.
Mention improved accuracy, robustness to noise and outliers, and ability to handle high-dimensional data. Also note limitations like reduced interpretability and increased computation.
Summarize that ensemble methods like random forests trade off interpretability and computational cost for better generalization and stability compared to single models.
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