BMC Bioinformatics

Table 3 Performance comparison of different ML and NN models for different types of error (e₁,e₂,e₃)

From: MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks

(e₁,e₂,e₃)	SVM	GB	RF	MNB	LR1	LR2	MLP	CNN
F1-micro
(0.5, 0.1, 0.4)	0.96	0.79	0.98	0.98	0.30	0.98	0.98	0.75
(0.5, 0.4, 0.1)	0.99	0.82	1.00	1.00	0.43	1.00	1.00	0.81
(0.3, 0.1, 0.4)	0.98	0.87	0.98	0.99	0.54	0.99	0.99	0.74
(0.0, 0.7, 0.2)	0.99	0.83	1.00	1.00	0.66	1.00	1.00	0.86
(0.0, 0.2, 0.7)	0.89	0.58	0.81	0.91	0.51	0.87	0.91	0.59

We consider several existing supervised ML methods, as well as NN models (i.e., MLP and CNN). For each experiment, we use 10-fold cross-validation. We use F1-micro to quantify the performance as defined in Classification performance metrics. Bold values represent the best results

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