Skip to main content

Table 1 Machine learning model comparison (values in the boxes are prediction accuracies on test data; higher values are better) (* 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 41.87% 63.87% 18.00% 52.67%
Acidogens 91.20% 97.07% 53.07% 99.73%
Hydrolyzers 65.60% 67.20% 10.67% 57.07%
Methanogens 85.17% 44.75% 89.33% 76.83%
Overall 71.26% 60.44% 53.55% 70.55%