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Table 5 Evaluation of all the methods by cross-validation on Boston dataset at genus level

From: Prediction of microbial communities for urban metagenomics using neural network approach

Evaluation metric

Methods

Precision

Recall

F1 score

Ranking loss

IDW

0.5316

0.6177

0.5691

0.1929

kNN

0.7359

0.6266

0.6723

0.1837

SVM

0.7583

0.5366

0.6282

0.1473

Random Forest (RF)

0.7318

0.6214

0.6682

0.1630

Neural Network

0.7228

0.5594

0.6214

0.1297

MetaMLAnn

0.7674

0.6706

0.7095

0.1270

MetaMLAnn+ RF

0.7744

0.6862

0.7229

0.1283

  1. Higher precision, recall, F1 score, and lower ranking loss indicate better performance. Bold entries indicate best performance among different methods
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Human Genomics

ISSN: 1479-7364