Fangorn Forest (F2): a machine learning approach to classify genes and genera in the family Geminiviridae

BMC Bioinformatics

Table 3 Performance of the genus classification model using default parameters of Weka

Type of evaluation	ML algorithm	Weighted average among all classes
Type of evaluation	ML algorithm	Accuracy	Precision	Recall	F-Measure	MCC	AUC
Using a test set	MLP	0.941	0.963	0.941	0.951	0.8940	0.971
	SMO	0.835	0.865	0.835	0.795	0.6340	0.816
	RF	0.934	0.941	0.934	0.936	0.8750	0.988
10-fold cross validation	MLP	0.970	0.970	0.971	0.970	0.9610	0.991
	SMO	0.920	0.901	0.920	0.906	0.8850	0.962
	RF	0.966	0.966	0.966	0.965	0.9510	0.997
Leave-one-out	MLP	0,971	0,971	0,972	0,960	0.9920	0.995
	SMO	0.944	0.938	0.945	0.939	0.8810	0.946
	RF	0.991	0.991	0.991	0.991	0.9550	0.999
Mean performance	MLP	0.966	0.974	0.967	0.970	0.9490	0.986
	SMO	0.900	0.901	0.900	0.880	0.8800	0.908
	RF	0.964	0.966	0.964	0.964	0.9238	0.995

ISSN: 1471-2105