Explain the bias-variance tradeoff in machine learning.

The bias-variance tradeoff describes the relationship between model complexity and generalization error components.\n\n• Bias: Error from oversimplified assumptions (underfitting)\n• Variance: Error from sensitivity to training data fluctuations (overfitting)\n• Total Error = Bias² + Variance + Irreducible Error\n• Sweet spot: Balance complexity to minimize total error\n\nExample: Linear regression (high bias, low variance) vs deep neural networks (low bias, high variance). Use cross-validation and learning curves to find optimal model complexity.