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Table 2 Micro- and macro-average performance measures across 19 experiments using feed-forward neural networks

From: Machine Learning for detection of viral sequences in human metagenomic datasets

Method Class Precision Recall F1-score
Micro-average Non virus 0.97 1.00 0.99
Micro-average Virus 0.69 0.13 0.21
Macro-average Non virus 0.96 1.00 0.98
Macro-average Virus 0.43 0.12 0.19
  1. The results are from the best model according to area under the ROC curve (ROCmicro=0.790). This model used two 1024 units FC layers with Relu nonlinearity, 0.25 dropout rate and class_weight_power 0.25 (see Additional file 3). All networks were trained for 10 epochs, using Adam optimizer with 10e−4 initial learning rate that was multiplied with 0.95 after each epoch