Table 4 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 C
From: Genome-wide prediction of discrete traits using bayesian regressions and machine learning
Parameter | Method | α (number of records) | |||
|---|---|---|---|---|---|
0.05 (7) | 0.10 (24) | 0.25 (80) | 0.50 (104) | ||
Specificity1 | TBA | 1 | 0.50 | 0.64 | 0.71 |
BL | 0 | 0.25 | 0.61 | 0.71 | |
RF | 1 | 0.75 | 0.75 | 0.71 | |
L2B | 1 | 1 | 0.96 | 0.98 | |
LhB | 1 | 1 | 0.82 | 0.69 | |
Sensitivity1 | TBA | 0.33 | 0.30 | 0.54 | 0.53 |
BL | 0.5 | 0.30 | 0.44 | 0.43 | |
RF | 0.33 | 0.35 | 0.52 | 0.51 | |
L2B | 0.17 | 0.20 | 0.15 | 0.15 | |
LhB | 0.33 | 0.20 | 0.46 | 0.45 | |
Phi correlation1 | TBA | 0.26 | -0.16 | 0.17 | 0.24 |
BL | -0.35 | -0.35 | 0.05 | 0.15 | |
RF | 0.26 | 0.08 | 0.26 | 0.23 | |
L2B | 0.17 | 0.20 | 0.17 | 0.24 | |
LhB | 0.26 | 0.20 | 0.28 | 0.15 | |
Misclassification rate (%)2 | TBA | 57 | 67 | 43 | 38 |
BL | 57 | 71 | 50 | 43 | |
RF | 57 | 58 | 40 | 39 | |
L2B | 71 | 67 | 56 | 44 | |
LhB | 57 | 67 | 41 | 43 | |