Pipeline
Compete Name:simple-neural-net-model
Pipeline Name:
Experimental Results
| Pipeline ID | Execution time | Memory | Score | Library & Version |
|---|---|---|---|---|
| 36188 | 75.7975965 | 1539.1208925247 | 0.98 | numpy,1.17.4 |
| 36188 | 75.7975965 | 1539.1208925247 | 0.98 | scikit-learn,1.0.1 |
| 36188 | 72.8717828 | 1541.8546676636 | 0.980525 | numpy,1.17.4 |
| 36188 | 72.8717828 | 1541.8546676636 | 0.980525 | scikit-learn,0.24.2 |
| 36188 | 85.61052450000001 | 1541.7327470779 | 0.973975 | numpy,1.17.4 |
| 36188 | 85.61052450000001 | 1541.7327470779 | 0.973975 | scikit-learn,0.23.2 |
| 36188 | 104.7661995 | 1538.401260376 | 0.980425 | numpy,1.17.4 |
| 36188 | 104.7661995 | 1538.401260376 | 0.980425 | scikit-learn,0.22.1 |
| 36188 | 112.3775695 | 1538.3960313797 | 0.979375 | numpy,1.17.4 |
| 36188 | 112.3775695 | 1538.3960313797 | 0.979375 | scikit-learn,0.22 |
| 36188 | 78.7393556 | 1538.9939718246 | 0.98095 | numpy,1.17.4 |
| 36188 | 78.7393556 | 1538.9939718246 | 0.98095 | scikit-learn,0.21.3 |
| 36188 | 104.6076614 | 1539.0786991119 | 0.9802 | numpy,1.17.4 |
| 36188 | 104.6076614 | 1539.0786991119 | 0.9802 | scikit-learn,0.20.3 |
| 36188 | 80.9098864 | 1538.7292556763 | 0.976675 | numpy,1.17.4 |
| 36188 | 80.9098864 | 1538.7292556763 | 0.976675 | scikit-learn,0.19.2 |
| 36188 | 82.655975 | 1539.9792194366 | 0.98025 | numpy,1.18.5 |
| 36188 | 82.655975 | 1539.9792194366 | 0.98025 | scikit-learn,1.0.1 |
| 36188 | 84.5118423 | 1542.7961511612 | 0.979625 | numpy,1.18.5 |
| 36188 | 84.5118423 | 1542.7961511612 | 0.979625 | scikit-learn,0.24.2 |
| 36188 | 112.9239719 | 1542.6713628769 | 0.9822 | numpy,1.18.5 |
| 36188 | 112.9239719 | 1542.6713628769 | 0.9822 | scikit-learn,0.23.2 |
| 36188 | 102.1052782 | 1539.3389558792 | 0.979625 | numpy,1.18.5 |
| 36188 | 102.1052782 | 1539.3389558792 | 0.979625 | scikit-learn,0.22.1 |
| 36188 | 95.6355915 | 1539.334151268 | 0.978225 | numpy,1.18.5 |
| 36188 | 95.6355915 | 1539.334151268 | 0.978225 | scikit-learn,0.22 |
| 36188 | 79.7822146 | 1539.8660135269 | 0.982425 | numpy,1.18.5 |
| 36188 | 79.7822146 | 1539.8660135269 | 0.982425 | scikit-learn,0.21.3 |
| 36188 | 153.00152920000002 | 1539.9636392593 | 0.979 | numpy,1.18.5 |
| 36188 | 153.00152920000002 | 1539.9636392593 | 0.979 | scikit-learn,0.20.3 |
| 36188 | 286.2815813 | 1539.6671104431 | 0.977475 | numpy,1.18.5 |
| 36188 | 286.2815813 | 1539.6671104431 | 0.977475 | scikit-learn,0.19.2 |
| 36188 | 297.83949739999997 | 1540.1168985367 | 0.979825 | numpy,1.19.5 |
| 36188 | 297.83949739999997 | 1540.1168985367 | 0.979825 | scikit-learn,1.0.1 |
| 36188 | 107.77826730000001 | 1542.9127130508 | 0.97975 | numpy,1.19.5 |
| 36188 | 107.77826730000001 | 1542.9127130508 | 0.97975 | scikit-learn,0.24.2 |
| 36188 | 83.8911292 | 1542.7674770355 | 0.9797 | numpy,1.19.5 |
| 36188 | 83.8911292 | 1542.7674770355 | 0.9797 | scikit-learn,0.23.2 |
| 36188 | 77.6932174 | 1539.4562978745 | 0.976625 | numpy,1.19.5 |
| 36188 | 77.6932174 | 1539.4562978745 | 0.976625 | scikit-learn,0.22.1 |
| 36188 | 89.9439832 | 1539.450425148 | 0.97955 | numpy,1.19.5 |
| 36188 | 89.9439832 | 1539.450425148 | 0.97955 | scikit-learn,0.22 |
| 36188 | 124.732605 | 1540.0031900406 | 0.9834 | numpy,1.19.5 |
| 36188 | 124.732605 | 1540.0031900406 | 0.9834 | scikit-learn,0.21.3 |
| 36188 | 126.8727644 | 1540.079832077 | 0.981575 | numpy,1.19.5 |
| 36188 | 126.8727644 | 1540.079832077 | 0.981575 | scikit-learn,0.20.3 |
| 36188 | 81.0296653 | 1539.7625894547 | 0.983575 | numpy,1.19.5 |
| 36188 | 81.0296653 | 1539.7625894547 | 0.983575 | scikit-learn,0.19.2 |
