Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm

Table 4

Performance metrics of proposed MLAs for the seven varieties of dry beans.

ML classifiers	Classes	Precision	Recall	f1-score
Random forest	Sira	0.85	0.83	0.84
	Bombay	1	1	1
	Dermason	0.92	0.91	0.91
	Barbunya	0.93	0.94	0.94
	Horoz	0.94	0.95	0.94
	CAli	0.94	0.95	0.95
	Seker	0.95	0.94	0.94
XGBoost	Sira	0.83	0.84	0.84
	Bombay	1	1	1
	Dermason	0.93	0.89	0.91
	Barbunya	0.95	0.95	0.95
	Horoz	0.93	0.96	0.94
	Cali	0.95	0.97	0.96
	Seker	0.95	0.93	0.94
CatBoost	Sira	0.88	0.84	0.86
	Bombay	1	1	1
	Dermason	0.94	0.94	0.94
	Barbunya	0.94	0.94	0.94
	Horoz	0.94	0.97	0.95
	Cali	0.94	0.97	0.95
	Seker	0.95	0.93	0.94