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The DeLong et al. method uses the test dataset to estimate the AUC performance and its uncertainty due to the finite test set.
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We actually used the test dataset produced by the same wooden bar, and the test accuracy was improved regardless of the DNN architecture.
We trained the fusion model with the scores computed over the training dataset, and we used the test dataset to compute the SER.
The CSIDOP method performance is assessed using the test dataset of 3812 human interaction pairs.
The predictive power of the model was first evaluated using the test dataset, comprised of the remaining 20% of the 1961 1990 long term average climate data (n = 1054).
The algorithms' performances were evaluated using the test dataset.
Models were developed using the "training" dataset and internally validated using the "test" dataset.
The tests of model performance and fitness were conducted using the test dataset.
Tests of model performance and model fitness were conducted using the test dataset.
Validation of the risk score was performed using the test dataset.
As expected, accuracies declined using the test dataset, but overall remained high, particularly for Plants.
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