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Table 3 Comparison of random forest with other classifiers

From: ReRF-Pred: predicting amyloidogenic regions of proteins based on their pseudo amino acid composition and tripeptide composition

 

ACC

SE

SP

MCC

Random forest

0.823

0.640

0.926

0.606

AdaBoost

0.751

0.605

0.834

0.450

Bagging

0.796

0.650

0.879

0.549

Naïve Bayes

0.773

0.693

0.818

0.510

LibSVM

0.796

0.585

0.916

0.545

Decision tree

0.779

0.658

0.848

0.515

LWL

0.732

0.581

0.817

0.408

JRip

0.773

0.583

0.880

0.492

KNN (K = 3)

0.791

0.566

0.918

0.532

MLP

0.454

0.734

0.296

0.031