31751

Pipeline

Compete Name:competition-1

Pipeline Name:

Experimental Results

Pipeline ID	Execution time	Memory	Score	Library & Version
31751	14.2956175	64.2776193619	21.47249239543726	numpy,1.17.4
31751	14.2956175	64.2776193619	21.47249239543726	scikit-learn,1.0.1
31751	14.251233	66.6873979568	21.47249239543726	numpy,1.17.4
31751	14.251233	66.6873979568	21.47249239543726	scikit-learn,0.24.2
31751	14.359623299999999	66.4867048264	21.47249239543726	numpy,1.17.4
31751	14.359623299999999	66.4867048264	21.47249239543726	scikit-learn,0.23.2
31751	13.788805700000001	62.9287157059	21.47249239543726	numpy,1.17.4
31751	13.788805700000001	62.9287157059	21.47249239543726	scikit-learn,0.22.1
31751	13.7955303	62.9128465652	21.47249239543726	numpy,1.17.4
31751	13.7955303	62.9128465652	21.47249239543726	scikit-learn,0.22
31751	13.871808	63.2421102524	21.47249239543726	numpy,1.17.4
31751	13.871808	63.2421102524	21.47249239543726	scikit-learn,0.21.3
31751	13.3717172	61.3175764084	21.47249239543726	numpy,1.17.4
31751	13.3717172	61.3175764084	21.47249239543726	scikit-learn,0.20.3
31751	13.4694323	59.8505897522	21.47249239543726	numpy,1.17.4
31751	13.4694323	59.8505897522	21.47249239543726	scikit-learn,0.19.2
31751	14.1568805	65.1758069992	21.47249239543726	numpy,1.18.5
31751	14.1568805	65.1758069992	21.47249239543726	scikit-learn,1.0.1
31751	14.0780523	67.593791008	21.47249239543726	numpy,1.18.5
31751	14.0780523	67.593791008	21.47249239543726	scikit-learn,0.24.2
31751	14.1272502	67.392870903	21.47249239543726	numpy,1.18.5
31751	14.1272502	67.392870903	21.47249239543726	scikit-learn,0.23.2
31751	13.9213345	63.8320770264	21.47249239543726	numpy,1.18.5
31751	13.9213345	63.8320770264	21.47249239543726	scikit-learn,0.22.1
31751	13.862787	63.8150720596	21.47249239543726	numpy,1.18.5
31751	13.862787	63.8150720596	21.47249239543726	scikit-learn,0.22
31751	13.904724400000001	64.1443624496	21.47249239543726	numpy,1.18.5
31751	13.904724400000001	64.1443624496	21.47249239543726	scikit-learn,0.21.3
31751	13.367148199999999	62.2197647095	21.47249239543726	numpy,1.18.5
31751	13.367148199999999	62.2197647095	21.47249239543726	scikit-learn,0.20.3
31751	13.5062739	60.7529125214	21.47249239543726	numpy,1.18.5
31751	13.5062739	60.7529125214	21.47249239543726	scikit-learn,0.19.2
31751	14.0828305	65.2955265045	21.47249239543726	numpy,1.19.5
31751	14.0828305	65.2955265045	21.47249239543726	scikit-learn,1.0.1
31751	14.0687293	67.7106800079	21.47249239543726	numpy,1.19.5
31751	14.0687293	67.7106800079	21.47249239543726	scikit-learn,0.24.2
31751	14.156874599999998	67.508890152	21.47249239543726	numpy,1.19.5
31751	14.156874599999998	67.508890152	21.47249239543726	scikit-learn,0.23.2
31751	13.8260953	63.9500494003	21.47249239543726	numpy,1.19.5
31751	13.8260953	63.9500494003	21.47249239543726	scikit-learn,0.22.1
31751	14.1128979	63.9328536987	21.47249239543726	numpy,1.19.5
31751	14.1128979	63.9328536987	21.47249239543726	scikit-learn,0.22
31751	13.9169501	64.2615251541	21.47249239543726	numpy,1.19.5
31751	13.9169501	64.2615251541	21.47249239543726	scikit-learn,0.21.3
31751	13.3592998	62.335392952	21.47249239543726	numpy,1.19.5
31751	13.3592998	62.335392952	21.47249239543726	scikit-learn,0.20.3
31751	13.492155	60.8688583374	21.47249239543726	numpy,1.19.5
31751	13.492155	60.8688583374	21.47249239543726	scikit-learn,0.19.2

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