The root mean squares error (RMSE) and mean absolute errors were 0.339 and 0.221, respectively, for the validation data set.
The mean square and mean absolute errors were 10.52 and 2.60 ppm for the testing set, respectively.
The root mean square errors (RMSE) and mean absolute errors for the validation data set were 0.042 and 0.024, respectively.
For evaluating the ability of the models, root mean square errors, mean absolute errors and determination coefficient statistics were used.
The RMSE and mean absolute errors in WNN model were 0.220 and 0.158 for prediction set and 0.156 and 0.114 for validation set.
Root mean square errors (RMSE), mean absolute errors (MAE) and correlation coefficient (R) statistics are used as comparing criteria for the evaluation of the models' performances.
Those three models that included combinations of FDXR, ACTN1 and DDB2 had the strongest information theoretic support, lowest mean absolute errors (≤0.13 Gy) and highest coefficients of determination (R2 = 0.96) (Supplementary Table S4).
The accuracy of the models was assessed by the coefficients of correlation (r), overall indices of model performance (OI), root mean square errors (RMSE), and mean absolute errors (MAE).
Experiments on three challenging benchmarks demonstrate our algorithm to be effective and superior than most low-level oriented state-of-the-arts in terms of precision-recall curves, F-measure and mean absolute errors.
Table 1 Normalized mean absolute errors (MAE) of centralized and distributed estimation algorithms.
Prediction accuracy was assessed by mean squared errors (MSEs) and mean absolute errors (MAEs).
