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Table 2 Area under the curve (AUC) values corresponding to Receiver Operating Characteristics (ROC) curves shown in Fig. 2 for test data (* Demonstrated deep learning model was a feed forward artificial neural network with three hidden layers)

From: Machine learning analysis of microbial flow cytometry data from nanoparticles, antibiotics and carbon sources perturbed anaerobic microbiomes

Putative Groups

Gradient Boosting

Naïve Bayes

Distributed Random Forests

Deep Learning*

Acetogens

0.7829

0.7279

0.6482

0.7853

Acidogens

0.9993

0.9999

0.9833

0.9983

Hydrolyzers

0.9638

0.9391

0.8055

0.9269

Methanogens

0.8520

0.8024

0.7773

0.8585