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Mathematics

Bias and Variance Tradeoff in Estimators

Quick fact

The bias-variance tradeoff is elegantly captured by the equation: Expected Test Error = Bias² + Variance + Irreducible Noise.

Why this is interesting

You’ve probably heard that “you can’t have your cake and eat it too.” In machine learning, that’s literally true for bias and variance—why can’t we just make both as small as possible?