Table 4.

The experimental matrix consists of training two different classifiers × three sampling methods × four targets, meaning there are 24 options (2 × 3× 4), each resampled 100× train–test splitting (giving a total of 600 models trained per target).

1. Classification Algorithm	2. Sampling Methods	3. Targets After Six Months of Treatment
(a) AdaBoost	(a) No sampling (base)	(a) MEF50 (t0 + 6)
(b) Random Forest	(b) Under sampling (cluster centroids)	(b) FEV1 (t0 + 6)
	(c) Oversampling	(c) FENO (t0 + 6)
		(d) LOAC (t0 + 6)

FEV1—forced expiratory volume in one second, MEF50- maximal expiratory flow at 50% of the vital flow capacity, FENO—Fractional Exhaled nitric oxide, LOAC—level of asthma control.