@wikipedia
A number characterising the prediction quality:
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R^2 = 1 - \frac{MSE(x, \hat x) = \frac{1}{n} \sum_{i=1}^n (x_i - \hat x_i)^2}{MSE(x, \bar x)} |
where
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| body | x = \{ x_1, \, x_2, \, x_3 , ... x_N \} |
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| a variable represented |
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by | by a discrete data set of numerical samples |
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body | | = \{ \hat x_1, \, \hat x_2, \, \hat x_3 , ... \hat x_N \} |
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discrete set of numerical samples of variable predictor of variable , represented by another discrete data set of numerical samples, with the same number of samples |
x | predicted at the same conditions as the original samples |
\hat \hat \hat \hat discrete set of predictors for the corresponding samples of variable | mean value of the variable , which can be considered as some sort of extreme predictor with zero variability |
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| mean square error between a variable and its predictor |
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