predictor of variable
discrete set of numerical samples of variable
discrete set of predictors for the corresponding samples of variable
A number characterising the prediction quality ( goodness of fit ):
MSE(x, \hat x) = \frac{1}{n} \sum_{i=1}^n (x_i - \hat x_i)^2 |
where
| a variable represented by data set | |
predictor of variable | |
discrete set of numerical samples of variable | |
discrete set of predictors for the corresponding samples of variable |
The MSE is a positive number, making zero for a constant dataset only.
The upper value of MSE is not limited and defined by the variable and its predictor, which can be troublesome in computations.
There are many normalised measures of prediction quality which are more comfortable for computations with Coefficient of determination (R2) being the most popular.
See also
Coefficient of determination (R2)