Table 3:

Machine learning predictive models^{^a}

Cohort	Ridge Regression			Gaussian Process			Support-Vector Machine			Random Forest			P Value
Cohort	r	R²	MAE	r	R²	MAE	r	R²	MAE	r	R²	MAE	P Value
Training	0.797	0.636	0.651	0.795	0.632	0.814	0.797	0.635	0.710	0.790	0.624	0.656	–
Internal test	0.741	0.549	0.725	0.733	0.537	0.874	0.734	0.538	0.754	0.734	0.539	0.740	.001^{^a}
External test	0.755	0.570	1.155	0.799	0.638	1.247	0.794	0.631	1.112	0.775	0.600	1.162	.009^{^b}

Note:—– indicates not available.
↵a F(1.82, 180.39) = 7.94. Partial η² = 0.07. df corrected using Greenhouse-Geisser estimates of sphericity (ε = 0.61).
↵b F(1.70, 175.52) = 5.25. Partial η² = 0.05. df corrected using Greenhouse-Geisser estimates of sphericity (ε = 0.57).