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The important technique of Model Validation is Cross-Validation (aka Blind Testing), which is widely used in scientific research and engineering practice.
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It should be noted though that Source Dataset may not hold enough of representative events/occurrences to provide the opportunity for Cross-Validation and in this case the Goodness of fit over the Training dataset will be the only one available, thus increasing the risk of future Model Prediction.
See also
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Natural Science / System / Model
[ Cross-Validation ][ Cross-Validation Plot ][ Goodness of fit ][ Mean Square Deviation (MSD) = Mean Square Error (MSE) ]
[ Root Mean Square Deviation (RMSD) = Root Mean Square Error (RMSE) ]
[ Average Relative Error (ARE) = Average Percentage Error (APE) ]
[ Average Absolute Relative Error (AARE) = Average Absolute Percentage Error (AAPE)]
[ Maximum Relative Error (MAXRE) = Maximum Percentage Error (MAXPE) ]
[ Maximum Absolute Relative Error (MAXARE) = Maximum Absolute Percentage Error (MAXAPE) ]
[ Coefficient of determination (R2) ][ Pearson correlation coefficient (ρP) ][ Correlation skewness Cross-Validation ]