What is the bias–variance tradeoff and how does it affect model performance?

Bias is error from overly simple assumptions; variance is error from sensitivity to training data. High bias → underfitting. High variance → overfitting. You balance this using: - Model choice and regularization - More data - Feature engineering - Cross-validation Interview tip: explain how you detect overfitting via training vs validation metrics.