Table 1.

Performance comparison of different modeling algorithms.

Modeling algorithms	95% CI of Pearson’s γ for h2 prediction	95% CI of Pearson’s γ for corr prediction
Kernel Ridge regression	0.837 ± 0.001	0.867 ± 0.001
Lasso	0.823 ± 0.002	0.793 ± 0.001
Huber regression	0.827 ± 0.001.	0.787 ± 0.001
Ridge regression	0.826 ± 0.001	0.795 ± 0.001
Random forest	0.854 ± 0.001.	0.856 ± 0.001
Support vector regression	0.856 ± 0.001	0.808 ± 0.001
AdaBoost random forest	0.858 ± 0.001	0.858 ± 0.001
Gradient boosting regression	0.870 ± 0.001	0.874 ± 0.001.