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 |