Pretty standard but you can still stumble if you just list buzzwords without explaining the tradeoffs.
Start by defining overfitting and its impact on model generalization, then systematically cover prevention techniques across data, model, and training levels. Emphasize that the choice of technique depends on the specific problem, data size, and model complexity, and mention how you would validate the effectiveness of these techniques.
Pro tip: At Meta, interviewers value practical experience with large-scale systems. Mention how you've applied these techniques in production, such as using regularization in a recommendation model to handle sparse data, and discuss trade-offs like increased training time or reduced model capacity.
Briefly explain what overfitting is and why it's problematic, e.g., poor performance on unseen data. This sets the stage for discussing prevention techniques.
Discuss methods like data augmentation, collecting more data, and ensuring data quality and diversity. Mention how these help the model generalize better.
Cover regularization methods (L1/L2, dropout), simplifying the model architecture, and using early stopping. Explain how these constrain the model's capacity.
Talk about cross-validation, ensemble methods (bagging, boosting), and hyperparameter tuning. Highlight how these improve robustness.
Emphasize the importance of monitoring validation metrics and discuss trade-offs between overfitting prevention and underfitting, as well as computational costs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.