Table 1. Performance of machine learning models in OVCAR-8 MCTS classifications. DT, decision tree. GB, gradient boosting. kNN, k nearest neighbor. LG, logistics. NB, naïve bayes. SVM, support vector machine. AH, agglomerative hierarchical. BC, birch. GM, Gaussian mixture. KM, k means. MBK, mini batch k-means. ST, spectral.

	Supervised						Unsupervised
Models	DT	GB	kNN	LG	NB	SVM	AH	BC	GM	KM	MBK	ST
Cross_Statistical
Accuracy	1.00	1.00	1.00	1.00	0.93	1.00	1.00	1.00	1.00	1.00	1.00	0.52
Precision	1.00	1.00	1.00	1.00	0.94	1.00	/	/	/	/	/	/
Recall	1.00	1.00	1.00	1.00	0.93	1.00	/	/	/	/	/	/
F1-score	1.00	1.00	1.00	1.00	0.93	1.00	/	/	/	/	/	/
Silhouette	/	/	/	/	/	/	0.44	0.44	0.44	0.44	0.44	0.05
Homogeneity	/	/	/	/	/	/	1.00	1.00	1.00	1.00	1.00	0.03
Completeness	/	/	/	/	/	/	1.00	1.00	1.00	1.00	1.00	0.15
V_meaure	/	/	/	/	/	/	1.00	1.00	1.00	1.00	1.00	0.04
Cross_Screening
Accuracy	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
Precision	1.00	1.00	1.00	1.00	1.00	1.00	/	/	/	/	/	/
Recall	1.00	1.00	1.00	1.00	1.00	1.00	/	/	/	/	/	/
F1-score	1.00	1.00	1.00	1.00	1.00	1.00	/	/	/	/	/	/
Silhouette	/	/	/	/	/	/	0.70	0.70	0.70	0.70	0.70	0.70
Homogeneity	/	/	/	/	/	/	1.00	1.00	1.00	1.00	1.00	1.00
Completeness	/	/	/	/	/	/	1.00	1.00	1.00	1.00	1.00	1.00
V_meaure	/	/	/	/	/	/	1.00	1.00	1.00	1.00	1.00	1.00
Composite_Hyperparameter
Accuracy	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
Precision	1.00	1.00	1.00	1.00	1.00	1.00	/	/	/	/	/	/
Recall	1.00	1.00	1.00	1.00	1.00	1.00	/	/	/	/	/	/
F1-score	1.00	1.00	1.00	1.00	1.00	1.00	/	/	/	/	/	/
Silhouette	/	/	/	/	/	/	0.72	0.72	0.72	0.72	0.72	0.72
Homogeneity	/	/	/	/	/	/	1.00	1.00	1.00	1.00	1.00	1.00
Completeness	/	/	/	/	/	/	1.00	1.00	1.00	1.00	1.00	1.00
V_meaure	/	/	/	/	/	/	1.00	1.00	1.00	1.00	1.00	1.00