Table 6.

Ranked ECFP6 Fingerprint-Based Prediction Scores for Each Machine Learning Algorithm by Metrics (Average over Three Datasets)^a

algorithms	AUC	F1_score	ACC	Cohen’s κ	MCC	precision	recall	mean	rank
SVM	0.912	0.572	0.909	0.511	0.544	0.479	0.793	0.674	9
MLP_1	0.947	0.619	0.922	0.568	0.601	0.534	0.838	0.719	3
MLP_2	0.938	0.609	0.920	0.552	0.582	0.516	0.818	0.705	5
MLP_3	0.943	0.610	0.923	0.564	0.598	0.530	0.828	0.714	4
MLP_4	0.940	0.605	0.922	0.554	0.582	0.542	0.782	0.704	6
MLP_5	0.932	0.600	0.915	0.544	0.583	0.505	0.848	0.704	6
RF	0.889	0.575	0.902	0.479	0.492	0.501	0.683	0.646	10
ABDT	0.932	0.573	0.907	0.520	0.560	0.489	0.827	0.687	8
DT	0.816	0.487	0.878	0.398	0.422	0.416	0.659	0.582	11
NB	0.957	0.626	0.923	0.572	0.603	0.536	0.838	0.722	2
logistic	0.948	0.624	0.924	0.574	0.609	0.527	0.856	0.723	1

^aEach bold entry shows the highest metric value among the machine learning models using different algorithms.