Explain the bias-variance tradeoff. How does it relate to model complexity, and how do you find the right balance?

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The bias-variance tradeoff is a fundamental concept that explains the sources of prediction error in machine learning models and guides decisions about model complexity. Bias refers to the error introduced by approximating a complex real-world problem with a simplified model.