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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