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Regarding the comprehensive evaluation of the three criteria, the best classification models are obtained by using gene selection method NMSC-MSC for learning classifier NMSC, NBC-MSC for NBC classifier, and NBC-MSC and SVMRFE for UDC classifier.
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The best classification model is trained through 3-fold cross validation and obtained for each tree.
To provide some understanding of how the best performing classification models are being trained to recognize VE recruitment, Table 7 shows a list of the top-weighted features in the logistic regression model.
Figures 5 and 6 show the box-plots of average testing values for EFSMO and OSMO, the classification models are based on the best training.
Classification models are frequent in QSAR modeling.
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].
Classification models were implemented with Weka [ 49].
Classification models were implemented in Weka [ 20].
The best sentiment classification model is then used in the rest of our analyses.
The best parameters for classification model were explored for the effective prediction.
In other words, the best classification models under different learning classifiers are associated with different methods of gene selection.
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