Sentence examples for mean square relative from inspiring English sources

Predicted parameters yield an average RMSD (root mean square relative deviation) of 40% from vapor pressure data.

Root mean square relative error (RMSRE) was calculated to indicate the prediction performance of the obtained models.

For the MLR model, the prediction root mean square relative error (RMSRE) of leave one out cross validation and external validation is 2.82 and 2.95, respectively.

These five error parameters are given in Table 2 (the absolute error, the minimum and maximum absolute error, the correlation coefficient, standard deviation and finally the root mean square relative error).

Four performance criteria are used in this study to assess the goodness of fit of the models, which are: root mean square error (RMSE), mean absolute error (MAE), mean square relative error (MSRE), and coefficient of determination (R 2) (further discussed by Ghorbani et al. 2012).

To evaluate the performance of regression methods, we measured the root mean square error (RMSE), root mean square relative error (RMSRE), Pearson product- moment correlation coefficient (Pearson Cor) and Spearman's rank correlation coefficient (Spearman Cor).

The results are summarized in table 4. *p-value < 0.001 (as compared to actual LOS) LOS= Length of stay LPF= Loss Penalty Function RMSRE= Root Mean Squared Relative Error The interpretation of the different validation measures can be found in the methodology section.

Second, the FA inaccuracy in the nine FA maps shown in Figures 6(a) to 6(i), estimated by their root mean square errors relative to the low-artifact reference scan (i.e., with 28 overscans and 4 averages), is (a) 0.19, (b) 0.24, (c) 0.43, (d) 0.19, (e) 0.22, (f) 0.25, (g) 0.18, (h) 0.21, and (i) 0.24.

A comparison among constructed models and previous models using the concepts of correlation coefficient, mean square error, average relative error and absolute average relative error reveals power-law committee machine outperforms all SVR, ACE, and previous models.

Root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute error (MAE) and determination coefficient (R2) were employed to evaluate the performance of conventional/single MARS, BN and GEP, as well as the proposed MARS-GARCH, BN-GARCH and GEP-GARCH hybrid models.

To compare between the models their root mean square error (RMSE), mean relative error (MRE) and mean absolute error (MAE) was found out.