Instruction: Describe what bias and variance are, and their impact on model performance.
Context: This question tests the candidate's understanding of the balance between bias and variance, and its importance in machine learning.
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The way I'd explain it in an interview is this: The bias-variance tradeoff is a way of thinking about underfitting and overfitting. High-bias models are too simple and miss important structure, so they make systematic errors. High-variance models are too sensitive to the training data, so they fit noise and become...
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