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Table 1 Performance statistics for the models described here and their beta binomial parameters a

From: Using beta binomials to estimate classification uncertainty for ensemble models

Data set Model Architect.b Threshold Sensitivity Specificity J c α β MRd
logP2 1 33×6×40 16.5 0.849 0.882 0.731 0.695c 0.772 0.083
  2 33×3×45 16.5 0.861 0.857 0.718 0.635 0.571 0.087
Ames 1 33×2×26 16.5 0.791 0.596 0.387 0.926 0.537 0.222
  2 33×4×24 16.5 0.700 0.676 0.376 0.489 0.462 0.239
logP3 1a 33×4×44 16.5 0.882 0.892 0.774 1.229 0.469 0.077
  1b 33×4×44 24.5 0.840 0.921 0.761 0.925 0.323 0.066
  2a 75×4×42 37.5 0.889 0.885 0.775 1.163 0.472 0.089
  2b 75x4x42 53.5 0.858 0.910 0.768 1.037 0.415 0.083
CYP2D6 1a 33×3×35 16.5 0.721 0.789 0.510 1.561 0.447 0.211
  1b 33×3×35 27.5 0.604 0.873 0.476 1.350 0.294 0.164
logP3 3a e 33×3×24 e 16.2 0.874 0.862 0.736 0.891 0.263 0.095
  3b 33×3×24 20.3 0.862 0.874 0.742 0.690 0.306 0.096
  1. aPerformance statistics are for compounds in the validation set. Beta binomial parameters shown are those obtained by fitting to the distribution of training pool misclassifications. b“Architect”. indicates the network architecture, given as networks × neurons × inputs. cJ is Youden’s index [28], which is equal to sensitivity + specificity – 1. d“MR” is the misclassification rate for the training pool. eBoldface names and architectures indicate models in which ensemble classifications were determined using the averaging method.