Understanding the Mean Squared Error and the Bias-Variance Tradeoff 📊

We define the mean squared error of an estimator. We show that the mean squared error is the sum of the variance of the estimator and the squared bias of the estimator. This proof shows that there is a tradeoff between bias and variance which cannot typically be avoided.

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