Table 1.

Statistics for the best models for various learners

Positive data	Learner	Recall	Precision	Sensitivity	Specificity	F-measure	Accuracy
Human	SVM	1.000	0.501	1.000	1.000	0.668	0.501
	DT	0.840	0.839	0.840	0.840	0.835	0.835
	MLP	0.835	0.835	0.835	0.835	0.835	0.835
	NB	0.845	0.843	0.845	0.845	0.838	0.837
	BLR	0.840	0.800	0.840	0.840	0.765	0.741
	RF	0.872	0.872	0.872	0.872	0.872	0.872
Rodent	SVM	1.000	0.501	1.000	1.000	0.668	0.501
	DT	0.859	0.851	0.859	0.859	0.840	0.837
	MLP	0.907	0.904	0.907	0.907	0.890	0.888
	NB	0.897	0.890	0.897	0.897	0.871	0.866
	BLR	0.864	0.812	0.864	0.864	0.761	0.728
	RF	0.918	0.918	0.918	0.918	0.916	0.916

Note: The best results for each learner given either human or rodent positive data with pseudo hairpins as negative data. RF performs best for this dataset (F-measure bolded). General performance among classifiers does not differ greatly even without parameter optimization. SVM, support vector machine; DT, decision tree; MLP, multi-layer perceptron; NB, naïve Bayes; BLR, Bayesian logistic regression; RF, random forest.