Instruction: Define overfitting and discuss strategies to avoid it.
Context: This question evaluates the candidate's understanding of a common problem in machine learning models and their knowledge of solutions to mitigate it.
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The way I'd explain it in an interview is this: Overfitting happens when the model learns the training data too literally, including noise and accidental patterns that do not generalize to new examples. You usually see it when training performance keeps improving but validation...