Table 9. Open in new tab Results of... | Oxford Academic

Table 9.

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Results of classification with four machine learning methods using five MINT-selected features listed in Table 2. Columns are as in Table 7.

Machine Learning algorithm results (five MINT-selected features)
	Completeness		Purity		F1 Score		Accuracy
	Galaxies	Point Sources	Galaxies	Point Sources	Galaxies	Point Sources
Random Forest	0.986	0.954	0.972	0.977	0.979	0.965	0.974
Adaboost	0.986	0.953	0.971	0.977	0.979	0.965	0.974
ExtraTrees	0.986	0.956	0.973	0.976	0.979	0.966	0.974
Gradient Boosted Trees	0.986	0.954	0.972	0.977	0.979	0.965	0.974

Machine Learning algorithm results (five MINT-selected features)
	Completeness		Purity		F1 Score		Accuracy
	Galaxies	Point Sources	Galaxies	Point Sources	Galaxies	Point Sources
Random Forest	0.986	0.954	0.972	0.977	0.979	0.965	0.974
Adaboost	0.986	0.953	0.971	0.977	0.979	0.965	0.974
ExtraTrees	0.986	0.956	0.973	0.976	0.979	0.966	0.974
Gradient Boosted Trees	0.986	0.954	0.972	0.977	0.979	0.965	0.974

Table 9.

Open in new tab

Results of classification with four machine learning methods using five MINT-selected features listed in Table 2. Columns are as in Table 7.

Machine Learning algorithm results (five MINT-selected features)
	Completeness		Purity		F1 Score		Accuracy
	Galaxies	Point Sources	Galaxies	Point Sources	Galaxies	Point Sources
Random Forest	0.986	0.954	0.972	0.977	0.979	0.965	0.974
Adaboost	0.986	0.953	0.971	0.977	0.979	0.965	0.974
ExtraTrees	0.986	0.956	0.973	0.976	0.979	0.966	0.974
Gradient Boosted Trees	0.986	0.954	0.972	0.977	0.979	0.965	0.974

Machine Learning algorithm results (five MINT-selected features)
	Completeness		Purity		F1 Score		Accuracy
	Galaxies	Point Sources	Galaxies	Point Sources	Galaxies	Point Sources
Random Forest	0.986	0.954	0.972	0.977	0.979	0.965	0.974
Adaboost	0.986	0.953	0.971	0.977	0.979	0.965	0.974
ExtraTrees	0.986	0.956	0.973	0.976	0.979	0.966	0.974
Gradient Boosted Trees	0.986	0.954	0.972	0.977	0.979	0.965	0.974