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  2. Mean squared error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_error

    The MSE can also be computed on q data points that were not used in estimating the model, either because they were held back for this purpose, or because these data have been newly obtained. Within this process, known as cross-validation, the MSE is often called the test MSE, [4] and is computed as

  3. Minimum mean square error - Wikipedia

    en.wikipedia.org/wiki/Minimum_mean_square_error

    For random vectors, since the MSE for estimation of a random vector is the sum of the MSEs of the coordinates, finding the MMSE estimator of a random vector decomposes into finding the MMSE estimators of the coordinates of X separately: ⁡ {(()) ()} =, for all i and j

  4. Mean squared prediction error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_prediction_error

    When the model has been estimated over all available data with none held back, the MSPE of the model over the entire population of mostly unobserved data can be estimated as follows.

  5. Mean absolute scaled error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_scaled_error

    Asymptotic normality of the MASE: The Diebold-Mariano test for one-step forecasts is used to test the statistical significance of the difference between two sets of forecasts. [ 5 ] [ 6 ] [ 7 ] To perform hypothesis testing with the Diebold-Mariano test statistic, it is desirable for D M ∼ N ( 0 , 1 ) {\displaystyle DM\sim N(0,1)} , where D M ...

  6. Cross-validation (statistics) - Wikipedia

    en.wikipedia.org/wiki/Cross-validation_(statistics)

    One by one, a set is selected as inner test (validation) set and the l - 1 other sets are combined into the corresponding inner training set. This is repeated for each of the l sets. The inner training sets are used to fit model parameters, while the outer test set is used as a validation set to provide an unbiased evaluation of the model fit.

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  8. Root mean square deviation - Wikipedia

    en.wikipedia.org/wiki/Root_mean_square_deviation

    In some disciplines, the RMSD is used to compare differences between two things that may vary, neither of which is accepted as the "standard". For example, when measuring the average difference between two time series x 1 , t {\displaystyle x_{1,t}} and x 2 , t {\displaystyle x_{2,t}} , the formula becomes

  9. Brier score - Wikipedia

    en.wikipedia.org/wiki/Brier_score

    in a set of N predictions, the Brier score measures the mean squared difference between: The predicted probability assigned to the possible outcomes for item i; The actual outcome ; Therefore, the lower the Brier score is for a set of predictions, the better the predictions are calibrated. Note that the Brier score, in its most common ...

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