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?