Statistics - (Average|Mean) Squared (MS) prediction error (MSE)

1 - About

The residual is a measure of prediction error in case of regression based on the residual and is a measure of model accuracy.

3 - Formula

3.1 - 1

(Average|Mean) Squared (MS) prediction error (of variance) of Mean Squared Error

<MATH> \begin{array}{rrl} \text{Mean Squared Error (MSE)} & = & \frac{\displaystyle \sum_{i=1}^{\href{sample_size}{N}}{(\href{raw_score}{Y}_i- \href{target}{\hat{Y}}_i)^2}}{\displaystyle \href{degree_of_freedom}{\text{Degree of Freedom}}} \\ & = & \frac{\displaystyle \sum_{i=1}^{\href{sample_size}{N}}{(\href{residual}{\text{Residual}}_i)^2}}{\displaystyle \href{degree_of_freedom}{\text{Degree of Freedom}}} \\ & = & (\href{Standard_Deviation}{\text{Standard Deviation}})^2 \end{array} </MATH>

3.2 - 2

The mean squared error is the squared bias plus the variance.

4 - Documentation / Reference

data_mining/mse.txt · Last modified: 2015/07/03 22:54 by gerardnico