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The predictive ability of the models was validated using the test set of 10 compounds that were not included in the training set of 51 compounds.
All decision parameters are trained using the training set, while all the experimental results are obtained using the test set.
We evaluated the accuracy of the method using the test set after an instance in the learning group is input.
The linear SVM is trained based on the training set by tenfold cross-validation strategy and tested using the test set.
The QSAR models were validated using the test set molecules, which did not participate in the descriptor selection and model fitting and calibration steps.
The classification models were generated using the training set of compounds followed by the validation of the best model using the test set of compounds [35].
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Meanwhile, external validation was performed by using the test sets for all models.
The results using the testing set are shown in Table 3.
The model with the best classification error is selected from the training set and the prediction error of that model is estimated using the testing set.
Finally, the performance of the classifier is evaluated using the testing set to determine how well the classifier performed by computing the accuracy.
A fitness value was determined for each model by using the testing set to compute the area under the receiver operating characteristic curve (AUC).
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