Bias-Variance Tradeoff

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machine-learning

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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.

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Bias measures how far off predictions are from the true values on average (underfitting). Variance measures how much predictions change across different training sets (overfitting). Increasing model complexity reduces bias but increases variance. The sweet spot minimizes total error (bias² + variance + irreducible noise).

Bias-Variance Tradeoff

Multiple Choice

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