What is overfitting and what techniques can be used to prevent it?
Direct Answer
A core machine learning concept question regarding model generalization and robustness.
Why Interviewers Ask This
Overfitting is a common pitfall where a model memorizes noise instead of learning patterns. Interviewers check if you recognize the signs and know how to mitigate them to ensure reliable predictions.
How to Answer This Question
Define overfitting as high variance where training performance is much better than test performance. List prevention techniques like regularization, cross-validation, pruning, and increasing training data. Explain the trade-off with underfitting.
Key Points to Cover
- Define high variance/low bias
- Mention regularization methods
- Discuss data quantity and quality
Sample Answer
Overfitting occurs when a model learns the noise in the training data, performing well on training but poorly on unseen data. To prevent this, I use techniques like L1/L2 regularization, dropout in neural networks, and e…
Common Mistakes to Avoid
- Confusing overfitting with underfitting
- Only listing techniques without explaining
- Ignoring the bias-variance tradeoff
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