Genetics Selection Evolution

Table 2 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 A

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

Parameter	Method	α (number of records)
		0.05 (12)	0.10 (79)	0.25 (98)	0.50 (138)
Specificity¹	TBA	1	0.71	0.58	0.56
	BTL	1	0.94	0.75	0.74
	RF	1	0.88	0.78	0.79
	L₂B	0.75	0.71	0.64	0.65
	L_hB	0.75	0.71	0.61	0.67
Sensitivity¹	TBA	0.75	0.58	0.58	0.56
	BTL	0.75	0.53	0.53	0.47
	RF	1	0.52	0.52	0.46
	L₂B	0.75	0.48	0.48	0.51
	L_hB	0.50	0.45	0.45	0.42
Phi correlation¹	TBA	0.71	0.24	0.16	0.13
	BTL	0.71	0.39	0.27	0.22
	RF	1	0.33	0.29	0.26
	L₂B	0.48	0.16	0.12	0.17
	L_hB	0.24	0.13	0.06	0.09
Misclassification rate (%)²	TBA	17	39	42	43
	BTL	17	38	39	40
	RF	0	41	39	38
	L₂B	25	47	46	42
	L_hB	42	49	49	46

¹Higher value is desirable; the best value for each percentile is in bold face;
²Lower value is desirable; the best value for each percentile is in bold face;
TBA = Threshold Bayes A, BTL = Bayesian Threshold LASSO, RF = Random Forest; L₂B = L₂-boosting algorithm, L_hB = L_h-boosting algorithm

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