Pipeline
Compete Name:tps-nov-eda-logistic-regression
Pipeline Name:
Experimental Results
| Pipeline ID | Execution time | Memory | Score | Library & Version |
|---|---|---|---|---|
| 33805 | 1944.8405325 | 3217.9731588364 | 0.7489943688838097 | pandas,0.25.3 |
| 33805 | 1944.8405325 | 3217.9731588364 | 0.7489943688838097 | scikit-learn,1.0.1 |
| 33805 | 2086.9869780999998 | 3220.7007446289 | 0.7489943688838097 | pandas,0.25.3 |
| 33805 | 2086.9869780999998 | 3220.7007446289 | 0.7489943688838097 | scikit-learn,0.24.2 |
| 33805 | 1903.0997744 | 3220.56021595 | 0.7489943688838097 | pandas,0.25.3 |
| 33805 | 1903.0997744 | 3220.56021595 | 0.7489943688838097 | scikit-learn,0.23.2 |
| 33805 | 1933.5592201000002 | 3217.3396940231 | 0.7489943688838097 | pandas,0.25.3 |
| 33805 | 1933.5592201000002 | 3217.3396940231 | 0.7489943688838097 | scikit-learn,0.22.1 |
| 33805 | 1980.2933483 | 3217.3334341049 | 0.7489943688838097 | pandas,0.25.3 |
| 33805 | 1980.2933483 | 3217.3334341049 | 0.7489943688838097 | scikit-learn,0.22 |
| 33805 | 1902.9146328 | 3217.8719453812 | 0.7489500285921704 | pandas,0.25.3 |
| 33805 | 1902.9146328 | 3217.8719453812 | 0.7489500285921704 | scikit-learn,0.21.3 |
| 33805 | 1907.2913033 | 3218.8461294174 | 0.7489717742322468 | pandas,0.25.3 |
| 33805 | 1907.2913033 | 3218.8461294174 | 0.7489717742322468 | scikit-learn,0.20.3 |
| 33805 | 1937.2266406 | 3217.5754241943 | 0.7489717742322468 | pandas,0.25.3 |
| 33805 | 1937.2266406 | 3217.5754241943 | 0.7489717742322468 | scikit-learn,0.19.2 |
| 33805 | 1935.6078809 | 3217.4812698364 | 0.7489943688838097 | pandas,1.0.5 |
| 33805 | 1935.6078809 | 3217.4812698364 | 0.7489943688838097 | scikit-learn,1.0.1 |
| 33805 | 1898.8098119 | 3220.2038555145 | 0.7489943688838097 | pandas,1.0.5 |
| 33805 | 1898.8098119 | 3220.2038555145 | 0.7489943688838097 | scikit-learn,0.24.2 |
| 33805 | 1897.4121241999999 | 3220.062292099 | 0.7489943688838097 | pandas,1.0.5 |
| 33805 | 1897.4121241999999 | 3220.062292099 | 0.7489943688838097 | scikit-learn,0.23.2 |
| 33805 | 1946.9412264 | 3216.8596544266 | 0.7489943688838097 | pandas,1.0.5 |
| 33805 | 1946.9412264 | 3216.8596544266 | 0.7489943688838097 | scikit-learn,0.22.1 |
| 33805 | 2030.8758481 | 3216.8549594879 | 0.7489943688838097 | pandas,1.0.5 |
| 33805 | 2030.8758481 | 3216.8549594879 | 0.7489943688838097 | scikit-learn,0.22 |
| 33805 | 1916.0428008000001 | 3217.3873052597 | 0.7489500285921704 | pandas,1.0.5 |
| 33805 | 1916.0428008000001 | 3217.3873052597 | 0.7489500285921704 | scikit-learn,0.21.3 |
| 33805 | 1924.9390309 | 3218.355099678 | 0.7489717742322468 | pandas,1.0.5 |
| 33805 | 1924.9390309 | 3218.355099678 | 0.7489717742322468 | scikit-learn,0.20.3 |
| 33805 | 1895.7745206 | 3217.0934867859 | 0.7489717742322468 | pandas,1.0.5 |
| 33805 | 1895.7745206 | 3217.0934867859 | 0.7489717742322468 | scikit-learn,0.19.2 |
| 33805 | 1907.3916766 | 3218.0538797379 | 0.7489943688838097 | pandas,1.1.5 |
| 33805 | 1907.3916766 | 3218.0538797379 | 0.7489943688838097 | scikit-learn,1.0.1 |
| 33805 | 1946.8483740000001 | 3220.7725877762 | 0.7489943688838097 | pandas,1.1.5 |
| 33805 | 1946.8483740000001 | 3220.7725877762 | 0.7489943688838097 | scikit-learn,0.24.2 |
| 33805 | 2058.2949175000003 | 3220.6318368912 | 0.7489943688838097 | pandas,1.1.5 |
| 33805 | 2058.2949175000003 | 3220.6318368912 | 0.7489943688838097 | scikit-learn,0.23.2 |
| 33805 | 1899.4694121999999 | 3217.4243507385 | 0.7489943688838097 | pandas,1.1.5 |
| 33805 | 1899.4694121999999 | 3217.4243507385 | 0.7489943688838097 | scikit-learn,0.22.1 |
| 33805 | 1938.1532736000001 | 3217.4201049805 | 0.7489943688838097 | pandas,1.1.5 |
| 33805 | 1938.1532736000001 | 3217.4201049805 | 0.7489943688838097 | scikit-learn,0.22 |
| 33805 | 1918.0609448 | 3217.952296257 | 0.7489500285921704 | pandas,1.1.5 |
| 33805 | 1918.0609448 | 3217.952296257 | 0.7489500285921704 | scikit-learn,0.21.3 |
| 33805 | 1906.8617219999999 | 3218.9209547043 | 0.7489717742322468 | pandas,1.1.5 |
| 33805 | 1906.8617219999999 | 3218.9209547043 | 0.7489717742322468 | scikit-learn,0.20.3 |
| 33805 | 1897.8497834 | 3217.6564493179 | 0.7489717742322468 | pandas,1.1.5 |
| 33805 | 1897.8497834 | 3217.6564493179 | 0.7489717742322468 | scikit-learn,0.19.2 |
| 33805 | 1948.3255455 | 3218.6230192184 | 0.7489943688838097 | pandas,1.2.4 |
| 33805 | 1948.3255455 | 3218.6230192184 | 0.7489943688838097 | scikit-learn,1.0.1 |
| 33805 | 2043.2565593 | 3221.3392305374 | 0.7489943688838097 | pandas,1.2.4 |
| 33805 | 2043.2565593 | 3221.3392305374 | 0.7489943688838097 | scikit-learn,0.24.2 |
| 33805 | 1916.4584937 | 3221.1917982101 | 0.7489943688838097 | pandas,1.2.4 |
| 33805 | 1916.4584937 | 3221.1917982101 | 0.7489943688838097 | scikit-learn,0.23.2 |
| 33805 | 1937.0659073 | 3217.9885263443 | 0.7489943688838097 | pandas,1.2.4 |
| 33805 | 1937.0659073 | 3217.9885263443 | 0.7489943688838097 | scikit-learn,0.22.1 |
| 33805 | 2064.8232666 | 3217.9848098755 | 0.7489943688838097 | pandas,1.2.4 |
| 33805 | 2064.8232666 | 3217.9848098755 | 0.7489943688838097 | scikit-learn,0.22 |
| 33805 | 1900.9944188 | 3218.5162696838 | 0.7489500285921704 | pandas,1.2.4 |
| 33805 | 1900.9944188 | 3218.5162696838 | 0.7489500285921704 | scikit-learn,0.21.3 |
| 33805 | 1932.4763206999999 | 3219.4860010147 | 0.7489717742322468 | pandas,1.2.4 |
| 33805 | 1932.4763206999999 | 3219.4860010147 | 0.7489717742322468 | scikit-learn,0.20.3 |
| 33805 | 2079.2501275 | 3218.2247200012 | 0.7489717742322468 | pandas,1.2.4 |
| 33805 | 2079.2501275 | 3218.2247200012 | 0.7489717742322468 | scikit-learn,0.19.2 |
