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We note that the partitioning of the data into separate training and test datasets, as was done in our analysis, is recommended in the statistical literature in lieu of using the same dataset for both model fitting and performance evaluation; the latter approach can result in misleadingly positive results [ 28].
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Further details concerning logic regression's fitting procedures and performance in comparison to other machine-learning processes are available [ 21, 23].
Although Model 1 has the poorest fitting and prediction performance among the three models presented in this paper, its simplicity affords two advantages under these conditions.
To evaluate the fitting and prediction performances of these models, we used the Akaike information criteria (AIC) (Akaike 1974).
We evaluated the fitting and prediction performances of the combined function models, as well as their limitations.
Moreover, while the log + log model is superior to the exp + exp model, it is not superior to Models 1, 2, and 3 in terms of the fitting and prediction performances.
Both the fitting and prediction performances of the single exp and single log function are obviously poorer than those of Model 2 in Fig. 4c. Figure 3 shows the results of the log + exp model (Model 1) represented by Eq. (1).
Because the fitting and prediction performances of our combined logarithmic and exponential model were reasonably high, we confirmed that each station had observed a surface displacement that is a superposition of the contributions caused by multiple postseismic deformation mechanisms with different timescales, with differing contribution ratios of each mechanism at the different stations.
The fitting and predictive performances of model were measured by the squared correlation coefficient (q) and root mean square error (RMSE) for both the training set and the external test set.
Therefore, to balance the accuracy and efficiency, the application of M-RANSAC algorithm may be the optimal choice for RMC fitting, and the superior performance of this double-measure system is validated.
The results of seven group AHP methods of the theoretical example were evaluated by three new evaluation measures, satisfactory index and fitting performance index.
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