BMC Bioinformatics

Table 2 The performance of the classifiers on the training and test dataset

From: Predicting rifampicin resistance mutations in bacterial RNA polymerase subunit beta based on majority consensus

	Training set						Test set
	DT	kNN	NB	PNN	SVM	MC	DT	kNN	NB	PNN	SVM	MC
Recall	0.77	0.82	0.71	0.76	0.79	0.79	0.76	0.80	0.72	0.80	0.72	0.80
Precision	0.78	0.82	0.88	0.85	0.84	0.88	0.90	0.80	0.95	0.91	0.90	0.95
Specificity	0.58	0.64	0.80	0.74	0.70	0.78	0.85	0.62	0.92	0.85	0.85	0.92
F-measure	0.77	0.82	0.79	0.80	0.81	0.83	0.83	0.80	0.82	0.85	0.80	0.87
Accuracy	0.70	0.76	0.74	0.75	0.76	0.78	0.79	0.74	0.79	0.82	0.76	0.84
AUC	0.68	0.75	0.83	0.78	0.80	0.81	0.81	0.84	0.74	0.89	0.79	0.83

DT decision tree, kNN k nearest neighbors, NB naïve Bayes, PNN probabilistic neural network, SVM support vector machine, MC majority consensus, AUC area under the curve

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ISSN: 1471-2105

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