Table 2 The result of AUC (Area under Curve) and accuracy for models based on test set of 14 nucleoside derivatives

From: Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors

Models

Features

Test set performance

Accuracy

F1 Score

Precision

Recall

AUC

DT

Descriptor_ REF #

0.60

0.67

0.75

0.60

0.60

LR

Descriptor_ REF #

0.67

0.76

1.00

0.61

0.81

RF

Descriptor_ REF #

0.53

0.59

0.63

0.56

0.53

XGBoost

Descriptor_ REF #

0.60

0.57

0.50

0.67

0.61

  1. *LR Logistic regression, DT Decision tree, RF Random forest, XGBoost Extreme gradient boosting.
  2. #Descriptors-REF: Recursive feature elimination (REF) has different optimal descriptors for different Algorithms: LR, n = 34; XGBoost, n = 33; DT, n = 23; RF, n = 26.
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