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Table 3 Specificity, sensitivity, phi correlation and misclassification rate for each model at detecting different α and (1-α) percentiles of extreme animals in the testing set within line B

From: Genome-wide prediction of discrete traits using bayesian regressions and machine learning

Parameter Method α (number of records)
   0.05 (7) 0.10 (25) 0.25 (78) 0.50 (137)
Specificity1 TBA 0.75 0.86 0.74 0.75
  BTL 0.75 0.86 0.61 0.58
  RF 0.75 0.57 0.48 0.37
  L2B 1 0.71 0.57 0.48
  LhB 0.75 0.71 0.57 0.63
Sensitivity1 TBA 1 0.95 0.64 0.58
  BTL 1 1 0.75 0.75
  RF 1 1 0.95 0.94
  L2B 1 0.72 0.56 0.64
  LhB 0.67 0.78 0.73 0.69
Phi correlation1 TBA 0.75 0.80 0.34 0.34
  BTL 0.75 0.90 0.34 0.32
  RF 0.75 0.70 0.50 0.38
  L2B 1 0.40 0.12 0.12
  LhB 0.42 0.46 0.28 0.32
Misclassification rate (%)2 TBA 14 8 35 34
  BTL 14 4 29 32
  RF 14 12 19 31
  L2B 0 28 44 43
  LhB 29 24 32 36
  1. 1Higher value is desirable; the best value for each percentile is in bold face;
  2. 2Lower value is desirable; the best value for each percentile is in bold face;
  3. TBA = Threshold Bayes A, BTL = Bayesian Threshold LASSO, RF = Random Forest; L2B = L2-boosting algorithm, LhB = Lh-boosting algorithm