Table 2 Comparison of the quality of the machine learning models.

From: Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis

Model

Accuracy

Precision

Recall

F1 score

Train time (s)

AUC-ROC

Random forest

0.842

0.836

0.842

0.837

0.718

0.947

Gradient boosting

0.815

0.805

0.815

0.801

1.988

0.932

K-nearest neighbors

0.813

0.806

0.813

0.808

0.003

0.910

Support vector machine

0.696

0.543

0.696

0.609

1.508

0.879

Logistic regression

0.751

0.714

0.751

0.707

0.058

0.827

Decision tree

0.813

0.815

0.813

0.814

0.017

0.832

  1. AUC-ROC, area under the receiver operating characteristic curve.
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