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