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Table 1 The best performing D–M combinations according to all performance measures considered

From: chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models

Descriptor Model IE 300 IE 100 Spec Sens Error Rate PPV F1 AUC
Burd RF \(\checkmark\) \(\checkmark\) \(\checkmark\) \(\checkmark\) \(\checkmark\) \(\checkmark\) \(\checkmark\) \(\checkmark\)
Burd SVM \(\checkmark\) \(\checkmark\) \(\checkmark\) \(\checkmark\) \(\checkmark\)   \(\checkmark\)  
Phar RF \(\checkmark\) \(\checkmark\) \(\checkmark\)   \(\checkmark\) \(\checkmark\) \(\checkmark\) \(\checkmark\)
Burd KNN \(\checkmark\) \(\checkmark\) \(\checkmark\)      \(\checkmark\)
Phar SVM    \(\checkmark\)   \(\checkmark\) \(\checkmark\)   
  1. Check indicates the D–M combination was among the best performers according to a performance measure using a significance level of 0.05. Check minus indicates marginal significant difference between the D–M combination and the best performer (significance level between .01 and .05). Performance measures considered were: initial enhancement at 300 tests, initial enhancement at 100 tests, specificity, sensitivity, error rate, positive predictive value, F1 measure, and area under the receiver operating characteristic curve