Table 6 Results of evaluation criteria for ML models.

ML algorithms	Class	Accuracy (%)		Precision (%)		Recall (%)		F-1Score (%)
ML algorithms	Class	Mean	Std.	Mean	Std.	Mean	Std.	Mean	Std.
KNN	Non-Polished	-----	-----	94.45	0.52	89.05	0.79	91.67	0.53
	Polished	-----	-----	89.55	0.71	94.71	0.49	92.06	0.47
	Overall	91.87	0.48	92.01	0.47	91.87	0.48	91.86	0.48
DT	Non-Polished	-----	-----	87.74	0.75	86.62	0.82	87.18	0.54
	Polished	-----	-----	86.68	0.66	87.79	0.72	87.22	0.39
	Overall	87.20	0.43	87.22	0.43	87.20	0.43	87.20	0.43
ETs	Non-Polished	-----	-----	93.78	0.55	90.72	0.81	92.22	0.50
	Polished	-----	-----	90.93	0.73	93.92	0.50	92.40	0.41
	Overall	92.31	0.44	92.36	0.43	92.31	0.44	92.31	0.44
RF	Non-Polished	-----	-----	92.71	0.61	90.42	0.70	91.55	0.51
	Polished	-----	-----	90.57	0.63	92.82	0.53	91.68	0.42
	Overall	91.62	0.45	91.65	0.45	91.62	0.45	91.62	0.45
SVM	Non-Polished	-----	-----	96.11	0.53	94.82	0.46	95.46	0.37
	Polished	-----	-----	94.84	0.45	96.12	0.51	95.48	0.35
	Overall	95.47	0.36	95.48	0.36	95.47	0.36	95.47	0.36
BPNN	Non-Polished	-----	-----	96.45	0.69	95.55	0.76	95.99	0.34
	Polished	-----	-----	95.55	0.73	96.44	0.76	95.99	0.35
	Overall	96.10	0.34	96.02	0.33	95.99	0.34	95.99	0.34

Quick links

Search