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The best validation performance is obtained for mean square error is equal to 9.9962×10−11 at epoch 637.
The model which exhibits the best validation performance is chosen as the final model.
Similar(58)
The best validation performance was 92.43 at epoch 0. After epoch 0, the MSE of training continued to descend gradually until epoch 6.
Validation performance is similar on our two public databases.
In one version of this point of view, poor cross-validation performance is viewed as meaning that the data are not good enough, and data should therefore by chosen based on what works best.
The Learning set is utilized to create the models and select the best classifier, whose performance is evaluated on the Validation set by means of the Area under the Receiving Operating Curve (AUC).
As a short summary, the best performance is achieved with SVM 5-fold cross-validation and PCA-based feature selection, which gives an accuracy of 67.9%.
The best performance is shown in boldface.
The model with the best performance was validated on the validation set.
The SVM models were evaluated by a 6-fold cross-validation and the best performance was achieved with a window size s = 7.
The network with the best performance was selected for further validation testing in test set 2. The AUC based on performance in test set 2 was computed, and sensitivity and specificity were determined.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com