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Table 3 Comparison of GTB with other typical classifiers on primary ontology features

From: Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network

Method

Precision

Recall

F-Measure

MCC

AUC

GTB

0.526

0.53

0.523

0.052

0.528

kNN

0.514

0.514

0.513

0.028

0.516

SVM

0.509

0.491

0.478

-0.019

0.491

Logistic

0.506

0.506

0.506

0.012

0.504

Naive Bayes

0.479

0.479

0.478

-0.043

0.46

Random forest

0.499

0.499

0.478

-0.002

0.499

AdaBoost

0.501

0.501

0.425

0.002

0.497

LogitBoost

0.499

0.499

0.479

-0.002

0.495

  1. The boldface figures indicate that GTB achieves the best performance compared with other 7 typical classifiers trained on primary ontology features