I knew the ensemble part fine, bagging, multiple decision trees, averaging predictions.
Start by defining random forest as an ensemble of decision trees, then explain that feature selection occurs at two levels: during tree construction (random subset of features at each split) and via importance scores (e.g., Gini importance or permutation importance). Emphasize how this randomness reduces correlation among trees and improves generalization, and mention practical trade-offs like computational cost and interpretability.
Pro tip: At Amazon, interviewers value scalability and customer impact. Mention that random forest's feature selection is parallelizable and can handle high-dimensional data, but also note its limitations (e.g., bias towards categorical features with many levels) and when simpler models might be preferred.
Explain that it's an ensemble learning method that builds multiple decision trees on bootstrapped samples and aggregates their predictions (bagging).
Describe how at each node, a random subset of features (mtry) is considered for splitting, which decorrelates trees and reduces overfitting.
Mention that after training, feature importance can be computed via Gini impurity decrease (mean decrease in impurity) or permutation importance, which measures impact on accuracy.
Note that random feature selection adds randomness but improves robustness; however, it can miss important features if the subset size is too small, and importance scores can be biased.
Connect to scalability, parallel training, and how feature importance can guide feature engineering or model interpretation in production systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.