Pipeline
Compete Name:tpg-0122
Pipeline Name:
Experimental Results
| Pipeline ID | Execution time | Memory | Score | Library & Version |
|---|---|---|---|---|
| 31770 | 23.1609477 | 65.1983118057 | 13.106456989469212 | numpy,1.17.4 |
| 31770 | 23.1609477 | 65.1983118057 | 13.106456989469212 | scikit-learn,1.0.1 |
| 31770 | 22.5052664 | 64.8766345978 | 13.106456989469212 | numpy,1.17.4 |
| 31770 | 22.5052664 | 64.8766345978 | 13.106456989469212 | scikit-learn,0.24.2 |
| 31770 | 22.1993843 | 64.674331665 | 13.106456989469212 | numpy,1.17.4 |
| 31770 | 22.1993843 | 64.674331665 | 13.106456989469212 | scikit-learn,0.23.2 |
| 31770 | 21.945539999999998 | 63.529633522 | 13.106456989469212 | numpy,1.17.4 |
| 31770 | 21.945539999999998 | 63.529633522 | 13.106456989469212 | scikit-learn,0.22.1 |
| 31770 | 21.8572836 | 63.5154218674 | 13.106456989469212 | numpy,1.17.4 |
| 31770 | 21.8572836 | 63.5154218674 | 13.106456989469212 | scikit-learn,0.22 |
| 31770 | 22.244474 | 64.5729017258 | 13.106456989469212 | numpy,1.17.4 |
| 31770 | 22.244474 | 64.5729017258 | 13.106456989469212 | scikit-learn,0.21.3 |
| 31770 | 21.489116199999998 | 62.7509365082 | 13.106456989469212 | numpy,1.17.4 |
| 31770 | 21.489116199999998 | 62.7509365082 | 13.106456989469212 | scikit-learn,0.20.3 |
| 31770 | 22.204166999999998 | 65.1956186295 | 13.106456989469212 | numpy,1.18.5 |
| 31770 | 22.204166999999998 | 65.1956186295 | 13.106456989469212 | scikit-learn,1.0.1 |
| 31770 | 22.099001 | 64.8788337708 | 13.106456989469212 | numpy,1.18.5 |
| 31770 | 22.099001 | 64.8788337708 | 13.106456989469212 | scikit-learn,0.24.2 |
| 31770 | 22.2317745 | 64.6762456894 | 13.106456989469212 | numpy,1.18.5 |
| 31770 | 22.2317745 | 64.6762456894 | 13.106456989469212 | scikit-learn,0.23.2 |
| 31770 | 21.8696467 | 63.5333242416 | 13.106456989469212 | numpy,1.18.5 |
| 31770 | 21.8696467 | 63.5333242416 | 13.106456989469212 | scikit-learn,0.22.1 |
| 31770 | 21.8464958 | 63.5171318054 | 13.106456989469212 | numpy,1.18.5 |
| 31770 | 21.8464958 | 63.5171318054 | 13.106456989469212 | scikit-learn,0.22 |
| 31770 | 21.9522232 | 64.5748529434 | 13.106456989469212 | numpy,1.18.5 |
| 31770 | 21.9522232 | 64.5748529434 | 13.106456989469212 | scikit-learn,0.21.3 |
| 31770 | 21.363239 | 62.752158165 | 13.106456989469212 | numpy,1.18.5 |
| 31770 | 21.363239 | 62.752158165 | 13.106456989469212 | scikit-learn,0.20.3 |
| 31770 | 22.3097624 | 65.3210391998 | 13.106456989469212 | numpy,1.19.5 |
| 31770 | 22.3097624 | 65.3210391998 | 13.106456989469212 | scikit-learn,1.0.1 |
| 31770 | 22.152655 | 65.0001621246 | 13.106456989469212 | numpy,1.19.5 |
| 31770 | 22.152655 | 65.0001621246 | 13.106456989469212 | scikit-learn,0.24.2 |
| 31770 | 22.1982743 | 64.7978410721 | 13.106456989469212 | numpy,1.19.5 |
| 31770 | 22.1982743 | 64.7978410721 | 13.106456989469212 | scikit-learn,0.23.2 |
| 31770 | 21.826760099999998 | 63.6552715302 | 13.106456989469212 | numpy,1.19.5 |
| 31770 | 21.826760099999998 | 63.6552715302 | 13.106456989469212 | scikit-learn,0.22.1 |
| 31770 | 21.9037295 | 63.6388893127 | 13.106456989469212 | numpy,1.19.5 |
| 31770 | 21.9037295 | 63.6388893127 | 13.106456989469212 | scikit-learn,0.22 |
| 31770 | 22.1938701 | 64.6948328018 | 13.106456989469212 | numpy,1.19.5 |
| 31770 | 22.1938701 | 64.6948328018 | 13.106456989469212 | scikit-learn,0.21.3 |
| 31770 | 21.6102197 | 62.8729038239 | 13.106456989469212 | numpy,1.19.5 |
| 31770 | 21.6102197 | 62.8729038239 | 13.106456989469212 | scikit-learn,0.20.3 |
