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Table 7 Logistic regression performance

From: Machine learning methods for metabolic pathway prediction

Predictor

AUC

max. ACC

SN (max. ACC)

SP (max. ACC)

max. FM

PR (max. FM)

RC (max. FM)

random features (r = 50)

0.939

0.902

0.732

0.947

0.768

0.74

0.8

random forest (r = 50, c = 8)

0.946

0.909

0.734

0.955

0.779

0.765

0.796

HC-BIC feature selection

0.948

0.91

0.738

0.956

0.785

0.765

0.808

HC-AIC feature selection

0.949

0.911

0.753

0.953

0.787

0.771

0.804

bagged HC-BIC (c = 8)

0.951

0.912

0.744

0.956

0.786

0.763

0.812

  1. See Table 3 for description of column headings. HC-BIC = hill-climbing on Bayes information criterion; HC-AIC = hill-climbing on Akaike information criterion.