# Bias–variance Tradeoff

fleeting- External reference: https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff

— https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff

property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters

— https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff

bias–variance dilemma or bias–variance problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set:[1

— https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff

Ideally, one wants to choose a model that both accurately captures the regularities in its training data, but also generalizes well to unseen data. Unfortunately, it is typically impossible to do both simultaneously. Hi

— https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff