Structure your answer as a diagnostic flowchart: first confirm overfitting with learning curves, then apply remedies in order of increasing cost and complexity, explaining the trade-offs of each. Emphasize that the right approach depends on data size, model type, and whether you can collect more data.
Pro tip: Always start by checking if you can get more data or improve data quality—it's often the most effective and least discussed solution. Also, mention that some 'overfitting' is acceptable if validation performance is good enough for the business goal.
Use learning curves and validation metrics to confirm overfitting and understand its severity. Check if training performance is high while validation performance is significantly lower.
If possible, collect more data or use data augmentation. If not, consider reducing feature dimensionality or using simpler features to decrease the signal-to-noise ratio.
Use L1/L2 regularization, dropout, early stopping, or batch normalization. Choose based on model type and where overfitting occurs (e.g., dropout for neural networks).
Reduce model complexity by decreasing layers/parameters or switching to a simpler algorithm. Alternatively, use ensemble methods like bagging or boosting to reduce variance.
After each intervention, re-evaluate on a held-out set and monitor the bias-variance trade-off. Iterate until validation performance meets requirements without excessive underfitting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.