Bias-Variance Tradeoff

Increasing model complexity always improves performance on unseen data. A model with high bias underfits the training data, while a model with high variance overfits it. The goal is to balance both. Bias and variance are independent and can both be minimized simultaneously. Variance only matters for regression problems, not classification.