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The results using the testing set are shown in Table 3.
A general methodology is given on the design of GA in a particular problem based on defining the maximum variation of a problem, using the training set to estimate the average probability of a single run to the desired level of statistical confidence, and using the testing set to verify the required performance.
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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We evaluated the accuracy of the method using the test set after an instance in the learning group is input.
The QSAR models were validated using the test set molecules, which did not participate in the descriptor selection and model fitting and calibration steps.
We estimated α and β coefficients using the test set lesions group, the subset of features retained by feature selection methods, and the optimised classifiers obtained by training data set analysis and evaluating the accuracy of the MCS.
The predictive accuracy of this classifier is determined using the test (set aside) examples with the same set of features.
Sleep parameters showing the highest correlation values were selected and computed again using the test set consisting of all subjects.
For comparison, we present the results of a cTAKES-based system (Table 5) versus the results we obtained (Table 6) using the test set [ 17].
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