Table 6 Performance metrics of the ML algorithms trained with the dataset obtained in the field, with a sliding window of 10 s and a time step of 1s. Results ranked by descending accuracy.

From: Real-time eructation event prediction in livestock using head vibrations and machine-learning in an IoT wearable device

Model

AUC

Accuracy

Precision

Recall

F1-Score

RF

0.768

0.741

0.741

0.741

0.741

AB

0.744

0.703

0.709

0.703

0.701

LightGBM

0.729

0.691

0.692

0.691

0.691

GB

0.719

0.689

0.701

0.689

0.684

XGBoost

0.708

0.679

0.679

0.679

0.679

LR

0.657

0.646

0.647

0.646

0.645

kNN

0.656

0.631

0.634

0.631

0.629

Ridge

0.652

0.651

0.651

0.651

0.651

DT

0.635

0.635

0.636

0.635

0.635

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