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Table 2 Performance comparison of LightGBM with other machine learning methods

From: PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine

Dataset

Methods

ACC

SN

SP

ST

PRE

F1

MCC

AUC

PDNA-62

SVM

0.817

0.745

0.829

0.787

0.433

0.547

0.468

0.873

 

Adaboost

0.814

0.791

0.818

0.804

0.431

0.558

0.485

0.881

 

RF

0.817

0.782

0.822

0.802

0.435

0.559

0.486

0.883

 

LightGBM

0.815

0.863

0.806

0.835

0.438

0.581

0.523

0.912

PDNA-224

SVM

0.786

0.765

0.776

0.771

0.194

0.310

0.306

0.847

 

Adaboost

0.773

0.761

0.774

0.768

0.192

0.307

0.320

0.851

 

RF

0.814

0.750

0.819

0.784

0.226

0.347

0.351

0.864

 

LightGBM

0.800

0.833

0.797

0.815

0.224

0.353

0.383

0.896