Table 5.

Table of performance metrics of different ML algorithms for SCLC.

ML model	Class	SCLC
		Accuracy	Precision	Recall	F1 score
Logistic regression	0	0.83	0.76	0.68	0.68
	1	0.7	0.75	0.68	0.68
	W. Avg	0.76	0.76	0.68	0.68
k-nearest neigh-bours	0	0.85	0.77	0.7	0.84
	1	0.79	0.76	0.76	0.79
	W. Avg	0.82	0.77	0.73	0.82
Support vector machine	0	0.88	0.86	0.74	0.88
	1	0.82	0.84	0.87	0.71
	W. Avg	0.85	0.85	0.8	0.79
Random forest classifier	0	0.88	0.87	0.79	0.87
	1	0.77	0.83	0.88	0.79
	W. Avg	0.85	0.85	0.85	0.84
XgBoost algorithm	0	0.80	0.81	0.86	0.80
	1	0.79	0.80	0.79	0.85
	W. Avg	0.81	0.81	0.83	0.82
AdaBoost algorithm	0	0.85	0.82	0.80	0.84
	1	0.81	0.83	0.83	0.79
	W. Avg	0.83	0.83	0.81	0.81