Table 3.

F₁ Scores calculated for different combinations of regression and classification models. Calculated using Equations (2)–(6) on data that was held out for validation during model training. Cells are colored by the indicated score.

		Classification Model
		Logistic_class	NeurNet_class	RandFor_class	SVMgaus_class	SVMlin_class
Regression Model	FullLM	0.677	0.417	0.718	0.610	0.711
	GaussProc	0.416	0.401	0.394	0.288	0.535
	NeurNet	0.596	0.496	0.690	0.569	0.596
	RandFor	0.332	0.442	0.479	0.578	0.556
	RidgeLM	0.521	0.376	0.570	0.493	0.596
	SelectLM	0.600	0.531	0.545	0.493	0.596
	StepLM	0.695	0.502	0.619	0.614	0.616