Skip to main content

Table 2 Comparison of GTB with other typical classifiers on heterogenous network-derived 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 0.897 0.872 0.884 0.772 0.949
kNN 0.738 0.833 0.783 0.542 0.768
SVM 0.882 0.779 0.840 0.728 0.859
Logistic 0.499 0.527 0.510 0.014 0.520
Naive Bayes 0.504 0.988 0.770 0.086 0.508
Random forest 0.880 0.841 0.862 0.733 0.866
AdaBoost 0.878 0.854 0.863 0.732 0.866
LogitBoost 0.803 0.820 0.811 0.617 0.808
  1. The boldface figures indicate that GTB achieves the best performance compared with other typical classifiers on heterogenous network-derived features