Skip to main content

Table 2 Overall summary of the performance of the models

From: Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets

Dataset Classifier* TP% FP% TN% FN% Accuracy ROC area BCR # MCC $
AID1626 CSCNB 44.4 19.3 80.7 55.6 80.32% 0.707 0.625 0.061
  CSCRF 65.7 17.5 82.5 34.3 82.36% 0.823 0.741 0.122
  CSCSMO 51.2 18.8 81.2 48.8 80.88% 0.750 0.662 0.080
  MetaCostJ48 59.3 19.6 80.4 40.7 80.24% 0.690 0.698 0.097
AID1949 CSCNB 46.9 19.4 80.6 53.1 80.04% 0.712 0.637 0.080
  CSCRF 69.2 18.5 81.5 30.8 81.25% 0.825 0.753 0.162
  CSCSMO 54.1 19.9 80.1 45.9 79.63% 0.744 0.671 0.107
  MetaCostJ48 61.6 18.5 81.5 38.4 81.17% 0.718 0.715 0.138
AID1332 NB 39.4 19.5 80.5 60.6 74.31% 0.686 0.599 0.171
  CSCRF 81.8 17.3 82.7 18.2 82.57% 0.876 0.822 0.521
  CSCSMO 72.7 16.2 83.8 27.3 82.11% 0.806 0.782 0.469
  MetaCostJ48 72.7 17.3 82.7 27.3 81.19% 0.762 0.777 0.454
  1. * CSC denotes CostSensitiveClassifier, # BCR - Balanced Classification Rate,
  2. $ MCC - Matthews Correlation Coefficient
Back to article page