37758

Pipeline

Compete Name:getting-0-80-gradientboosting-40-lines-of-code

Pipeline Name:

Experimental Results

Pipeline ID	Execution time	Memory	Score	Library & Version
37758	6.399752	63.9267110825	0.7929844738355377	pandas,1.2.4
37758	6.399752	63.9267110825	0.7929844738355377	scikit-learn,1.0.1
37758	6.1154196999999995	63.6102743149	0.7929844738355377	pandas,1.2.4
37758	6.1154196999999995	63.6102743149	0.7929844738355377	scikit-learn,0.24.2
37758	6.1106249	63.4132909775	0.7929844738355377	pandas,1.2.4
37758	6.1106249	63.4132909775	0.7929844738355377	scikit-learn,0.23.2
37758	6.0709706	62.6333770752	0.7929844738355377	pandas,1.2.4
37758	6.0709706	62.6333770752	0.7929844738355377	scikit-learn,0.22.1
37758	6.0393369	62.6167287827	0.7929844738355377	pandas,1.2.4
37758	6.0393369	62.6167287827	0.7929844738355377	scikit-learn,0.22
37758	5.8332987	63.6305112839	0.7929844738355377	pandas,1.2.4
37758	5.8332987	63.6305112839	0.7929844738355377	scikit-learn,0.21.3
37758	5.5840228	61.8180217743	0.7929844738355377	pandas,1.2.4
37758	5.5840228	61.8180217743	0.7929844738355377	scikit-learn,0.20.3
37758	5.4469979	59.032957077	0.7929844738355377	pandas,1.2.4
37758	5.4469979	59.032957077	0.7929844738355377	scikit-learn,0.19.2
37758	6.2303868	63.3720159531	0.7929844738355377	pandas,1.1.5
37758	6.2303868	63.3720159531	0.7929844738355377	scikit-learn,1.0.1
37758	6.1015516	63.0587921143	0.7929844738355377	pandas,1.1.5
37758	6.1015516	63.0587921143	0.7929844738355377	scikit-learn,0.24.2
37758	6.0582909	62.8617839813	0.7929844738355377	pandas,1.1.5
37758	6.0582909	62.8617839813	0.7929844738355377	scikit-learn,0.23.2
37758	6.0502163	62.082034111	0.7929844738355377	pandas,1.1.5
37758	6.0502163	62.082034111	0.7929844738355377	scikit-learn,0.22.1
37758	6.0069073	62.065987587	0.7929844738355377	pandas,1.1.5
37758	6.0069073	62.065987587	0.7929844738355377	scikit-learn,0.22
37758	5.8215817	63.0790977478	0.7929844738355377	pandas,1.1.5
37758	5.8215817	63.0790977478	0.7929844738355377	scikit-learn,0.21.3
37758	5.538406200000001	61.268078804	0.7929844738355377	pandas,1.1.5
37758	5.538406200000001	61.268078804	0.7929844738355377	scikit-learn,0.20.3
37758	5.4592224	57.697180748	0.7929844738355377	pandas,1.1.5
37758	5.4592224	57.697180748	0.7929844738355377	scikit-learn,0.19.2
37758	6.2333692	62.8027191162	0.7929844738355377	pandas,1.0.5
37758	6.2333692	62.8027191162	0.7929844738355377	scikit-learn,1.0.1
37758	6.133443400000001	62.4848747253	0.7929844738355377	pandas,1.0.5
37758	6.133443400000001	62.4848747253	0.7929844738355377	scikit-learn,0.24.2
37758	6.0613594	62.2876329422	0.7929844738355377	pandas,1.0.5
37758	6.0613594	62.2876329422	0.7929844738355377	scikit-learn,0.23.2
37758	6.2667928999999996	61.5080404282	0.7929844738355377	pandas,1.0.5
37758	6.2667928999999996	61.5080404282	0.7929844738355377	scikit-learn,0.22.1
37758	6.235772300000001	61.4918680191	0.7929844738355377	pandas,1.0.5
37758	6.235772300000001	61.4918680191	0.7929844738355377	scikit-learn,0.22
37758	6.048938799999999	62.5048065186	0.7929844738355377	pandas,1.0.5
37758	6.048938799999999	62.5048065186	0.7929844738355377	scikit-learn,0.21.3
37758	5.73413	60.694062233	0.7929844738355377	pandas,1.0.5
37758	5.73413	60.694062233	0.7929844738355377	scikit-learn,0.20.3
37758	5.6391857	56.6580944061	0.7929844738355377	pandas,1.0.5
37758	5.6391857	56.6580944061	0.7929844738355377	scikit-learn,0.19.2
37758	8.008859	63.5158090591	0.7929844738355377	pandas,0.25.3
37758	8.008859	63.5158090591	0.7929844738355377	scikit-learn,1.0.1
37758	7.577943	63.1970977783	0.7929844738355377	pandas,0.25.3
37758	7.577943	63.1970977783	0.7929844738355377	scikit-learn,0.24.2
37758	7.6868095	63.0023326874	0.7929844738355377	pandas,0.25.3
37758	7.6868095	63.0023326874	0.7929844738355377	scikit-learn,0.23.2
37758	7.6852759	62.2125005722	0.7929844738355377	pandas,0.25.3
37758	7.6852759	62.2125005722	0.7929844738355377	scikit-learn,0.22.1
37758	7.7219948	62.2002220154	0.7929844738355377	pandas,0.25.3
37758	7.7219948	62.2002220154	0.7929844738355377	scikit-learn,0.22
37758	7.4656125	62.4981737137	0.7929844738355377	pandas,0.25.3
37758	7.4656125	62.4981737137	0.7929844738355377	scikit-learn,0.21.3
37758	7.2282344	60.6867303848	0.7929844738355377	pandas,0.25.3
37758	7.2282344	60.6867303848	0.7929844738355377	scikit-learn,0.20.3
37758	7.039723899999999	58.6246099472	0.7929844738355377	pandas,0.25.3
37758	7.039723899999999	58.6246099472	0.7929844738355377	scikit-learn,0.19.2
37758	7.9359072	63.5960102081	0.7929844738355377	pandas,0.24.2
37758	7.9359072	63.5960102081	0.7929844738355377	scikit-learn,1.0.1
37758	7.5218491	63.2760095596	0.7929844738355377	pandas,0.24.2
37758	7.5218491	63.2760095596	0.7929844738355377	scikit-learn,0.24.2
37758	7.7696324	63.0801763535	0.7929844738355377	pandas,0.24.2
37758	7.7696324	63.0801763535	0.7929844738355377	scikit-learn,0.23.2
37758	7.6911802	62.2986717224	0.7929844738355377	pandas,0.24.2
37758	7.6911802	62.2986717224	0.7929844738355377	scikit-learn,0.22.1
37758	7.4977168	62.2832670212	0.7929844738355377	pandas,0.24.2
37758	7.4977168	62.2832670212	0.7929844738355377	scikit-learn,0.22
37758	7.2365881	62.5815324783	0.7929844738355377	pandas,0.24.2
37758	7.2365881	62.5815324783	0.7929844738355377	scikit-learn,0.21.3
37758	6.976988599999999	60.7681913376	0.7929844738355377	pandas,0.24.2
37758	6.976988599999999	60.7681913376	0.7929844738355377	scikit-learn,0.20.3
37758	6.846897599999999	58.7515068054	0.7929844738355377	pandas,0.24.2
37758	6.846897599999999	58.7515068054	0.7929844738355377	scikit-learn,0.19.2
37758	6.289883	62.393819809	0.7929844738355377	pandas,0.23.4
37758	6.289883	62.393819809	0.7929844738355377	scikit-learn,1.0.1
37758	6.0988093	62.0830965042	0.7929844738355377	pandas,0.23.4
37758	6.0988093	62.0830965042	0.7929844738355377	scikit-learn,0.24.2
37758	6.130626	61.8827676773	0.7929844738355377	pandas,0.23.4
37758	6.130626	61.8827676773	0.7929844738355377	scikit-learn,0.23.2
37758	6.0482602	61.1047010422	0.7929844738355377	pandas,0.23.4
37758	6.0482602	61.1047010422	0.7929844738355377	scikit-learn,0.22.1
37758	6.0569404	61.0835590363	0.7929844738355377	pandas,0.23.4
37758	6.0569404	61.0835590363	0.7929844738355377	scikit-learn,0.22
37758	5.7816522	61.3792448044	0.7929844738355377	pandas,0.23.4
37758	5.7816522	61.3792448044	0.7929844738355377	scikit-learn,0.21.3
37758	5.5413551	59.5642166138	0.7929844738355377	pandas,0.23.4
37758	5.5413551	59.5642166138	0.7929844738355377	scikit-learn,0.20.3
37758	5.4588634	57.5569124222	0.7929844738355377	pandas,0.23.4
37758	5.4588634	57.5569124222	0.7929844738355377	scikit-learn,0.19.2

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We provide a list of PLC issues captured by us in real-world pipelines and popular ML libraries.

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