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The best classification model is trained through 3-fold cross validation and obtained for each tree.
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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.
The best sentiment classification model is then used in the rest of our analyses.
In the recursive addition of the features, for training samples, a classification model is one of the best methods.
The best parameters for classification model were explored for the effective prediction.
The identification of the best parameter set for each classification model was performed within the cross-validation procedure.
The strain classification model was 100% sensitive and >99% specific.
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.
Finally, among the 10 classification models, the one with the highest testing accuracy or highest cross-validation consistency was selected as the best classification 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.
Table 7 shows the bioassay datasets with the results of the best classification model highlighted.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com