Mean Square Error

Mean Square Error of an estimator
c is the unknown value of a parameter of a distribution, and X is an estimator of this parameter.

Where mean square error is and mean square total is for is the number of sample and is the number of coefficients in the model. Obviously, the relationship of R-squared and adjusted R-squared is .

It generally involves the minimization of some error function - say the total mean square error between the actual and expected output - under the supervision of a trainer. This is often called supervised training.

Here, we base this test on a comparison of the variance due to the between-groups variability (called Mean Square Effect, or MSeffect) with the within-group variability (called Mean Square Error, or Mserror; this term was first used by Edgeworth, ...

You can customize the parameters of the training process, namely: maximum number of cycles, a momentum value, a learning rate, a minimum value of mean square error (in other words "target error").

Define a cost function (e.g. mean square error)
where the activation y at the output layer is given by
and where ...

A unique feature of an adaptive filter is that its coefficient can be updated online according to some optimization criterion. One common criterion is to minimize the mean square error .

Analysis Services returns a table that reports statistics such as likelihood or root mean square error for individual partitions, and the mean and standard deviation of all measures for the aggregate models.

See also: Regression, Variance, Distribution, Residual, Normal distribution