Table 10. Open in new tab Results of... | Oxford Academic

Table 10.

Open in new tab

Results of classification for four machine learning methods using all available features in the catalogue. Columns are as in Table 7.

Machine Learning algorithm results (all features)
	Completeness		Purity		F1 Score		Accuracy
	Galaxies	Point Sources	Galaxies	Point Sources	Galaxies	Point Sources
Random Forest	0.990	0.964	0.978	0.983	0.984	0.973	0.980
Adaboost	0.989	0.964	0.978	0.982	0.984	0.973	0.980
ExtraTrees	0.990	0.966	0.979	0.983	0.985	0.975	0.981
Gradient Boosted Trees	0.989	0.968	0.980	0.982	0.985	0.975	0.981

Machine Learning algorithm results (all features)
	Completeness		Purity		F1 Score		Accuracy
	Galaxies	Point Sources	Galaxies	Point Sources	Galaxies	Point Sources
Random Forest	0.990	0.964	0.978	0.983	0.984	0.973	0.980
Adaboost	0.989	0.964	0.978	0.982	0.984	0.973	0.980
ExtraTrees	0.990	0.966	0.979	0.983	0.985	0.975	0.981
Gradient Boosted Trees	0.989	0.968	0.980	0.982	0.985	0.975	0.981

Table 10.

Open in new tab

Results of classification for four machine learning methods using all available features in the catalogue. Columns are as in Table 7.

Machine Learning algorithm results (all features)
	Completeness		Purity		F1 Score		Accuracy
	Galaxies	Point Sources	Galaxies	Point Sources	Galaxies	Point Sources
Random Forest	0.990	0.964	0.978	0.983	0.984	0.973	0.980
Adaboost	0.989	0.964	0.978	0.982	0.984	0.973	0.980
ExtraTrees	0.990	0.966	0.979	0.983	0.985	0.975	0.981
Gradient Boosted Trees	0.989	0.968	0.980	0.982	0.985	0.975	0.981

Machine Learning algorithm results (all features)
	Completeness		Purity		F1 Score		Accuracy
	Galaxies	Point Sources	Galaxies	Point Sources	Galaxies	Point Sources
Random Forest	0.990	0.964	0.978	0.983	0.984	0.973	0.980
Adaboost	0.989	0.964	0.978	0.982	0.984	0.973	0.980
ExtraTrees	0.990	0.966	0.979	0.983	0.985	0.975	0.981
Gradient Boosted Trees	0.989	0.968	0.980	0.982	0.985	0.975	0.981