The *mean squared error* as defined below is a common way
to measure the error of the estimation of some parameter, or,
more generally, the performance of a supervised learning algorithm
(for either regression or classification). Let
be
an estimator of the parameter of a data model. Then
*mean squared error (MSE)* of this estimator is

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Note that the middle term disappears as

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*variance error*:caused by the oversensitivity of a learning algorithm to small fluctuations in the training dataset due to random noise rather than the intended property of the data, a problem called

*overfitting*.*bias error*:caused by the under-sensitivity of a learning algorithm to the dominanting variation in the training dataset representing the essential relationship in the training dataset, a problem called

*underfitting*.