Pipeline
Compete Name:elasticnet-regression
Pipeline Name:
Experimental Results
| Pipeline ID | Execution time | Memory | Score | Library & Version |
|---|---|---|---|---|
| 32490 | 1.3517776530934498 | 64.5796146393 | 0.16202688778614519 | numpy,1.17.4 |
| 32490 | 1.3517776530934498 | 64.5796146393 | 0.16202688778614519 | scikit-learn,1.0.1 |
| 32490 | 1.3150096019962803 | 64.5740098953 | 0.16202688778614519 | numpy,1.17.4 |
| 32490 | 1.3150096019962803 | 64.5740098953 | 0.16202688778614519 | scikit-learn,0.24.2 |
| 32490 | 1.281662464956753 | 64.3792161942 | 0.16202688778614519 | numpy,1.17.4 |
| 32490 | 1.281662464956753 | 64.3792161942 | 0.16202688778614519 | scikit-learn,0.23.2 |
| 32490 | 1.2617498149629682 | 63.3093557358 | 0.16202688778614519 | numpy,1.17.4 |
| 32490 | 1.2617498149629682 | 63.3093557358 | 0.16202688778614519 | scikit-learn,0.22.1 |
| 32490 | 1.2733619690407068 | 63.3058576584 | 0.16202688778614519 | numpy,1.17.4 |
| 32490 | 1.2733619690407068 | 63.3058576584 | 0.16202688778614519 | scikit-learn,0.22 |
| 32490 | 1.2592950609978288 | 64.5187072754 | 0.16202688778614519 | numpy,1.17.4 |
| 32490 | 1.2592950609978288 | 64.5187072754 | 0.16202688778614519 | scikit-learn,0.21.3 |
| 32490 | 1.3841631771065295 | 65.1619606018 | 0.16202688778614519 | numpy,1.17.4 |
| 32490 | 1.3841631771065295 | 65.1619606018 | 0.16202688778614519 | scikit-learn,0.20.3 |
| 32490 | 1.2356819080887362 | 62.5711231232 | 0.16202688778614519 | numpy,1.17.4 |
| 32490 | 1.2356819080887362 | 62.5711231232 | 0.16202688778614519 | scikit-learn,0.19.2 |
| 32490 | 1.2438425099244341 | 64.5708999634 | 0.16202688778614519 | numpy,1.18.5 |
| 32490 | 1.2438425099244341 | 64.5708999634 | 0.16202688778614519 | scikit-learn,1.0.1 |
| 32490 | 1.304851278080605 | 64.5675821304 | 0.16202688778614519 | numpy,1.18.5 |
| 32490 | 1.304851278080605 | 64.5675821304 | 0.16202688778614519 | scikit-learn,0.24.2 |
| 32490 | 1.3019983579870313 | 64.3727283478 | 0.16202688778614519 | numpy,1.18.5 |
| 32490 | 1.3019983579870313 | 64.3727283478 | 0.16202688778614519 | scikit-learn,0.23.2 |
| 32490 | 1.227537297992967 | 63.3031158447 | 0.16202688778614519 | numpy,1.18.5 |
| 32490 | 1.227537297992967 | 63.3031158447 | 0.16202688778614519 | scikit-learn,0.22.1 |
| 32490 | 1.2547468750271946 | 63.2994632721 | 0.16202688778614519 | numpy,1.18.5 |
| 32490 | 1.2547468750271946 | 63.2994632721 | 0.16202688778614519 | scikit-learn,0.22 |
| 32490 | 1.2921749569941312 | 64.5118093491 | 0.16202688778614519 | numpy,1.18.5 |
| 32490 | 1.2921749569941312 | 64.5118093491 | 0.16202688778614519 | scikit-learn,0.21.3 |
| 32490 | 1.3212400319753215 | 65.1558895111 | 0.16202688778614519 | numpy,1.18.5 |
| 32490 | 1.3212400319753215 | 65.1558895111 | 0.16202688778614519 | scikit-learn,0.20.3 |
| 32490 | 1.25891335203778 | 62.5649290085 | 0.16202688778614519 | numpy,1.18.5 |
| 32490 | 1.25891335203778 | 62.5649290085 | 0.16202688778614519 | scikit-learn,0.19.2 |
| 32490 | 1.3095423870254308 | 64.6922864914 | 0.16202688778614519 | numpy,1.19.5 |
| 32490 | 1.3095423870254308 | 64.6922864914 | 0.16202688778614519 | scikit-learn,1.0.1 |
| 32490 | 1.3010889099678025 | 64.6865997314 | 0.16202688778614519 | numpy,1.19.5 |
| 32490 | 1.3010889099678025 | 64.6865997314 | 0.16202688778614519 | scikit-learn,0.24.2 |
| 32490 | 1.336507186992094 | 64.4920339584 | 0.16202688778614519 | numpy,1.19.5 |
| 32490 | 1.336507186992094 | 64.4920339584 | 0.16202688778614519 | scikit-learn,0.23.2 |
| 32490 | 1.2330423679668456 | 63.4215431213 | 0.16202688778614519 | numpy,1.19.5 |
| 32490 | 1.2330423679668456 | 63.4215431213 | 0.16202688778614519 | scikit-learn,0.22.1 |
| 32490 | 1.2961317710578442 | 63.4179763794 | 0.16202688778614519 | numpy,1.19.5 |
| 32490 | 1.2961317710578442 | 63.4179763794 | 0.16202688778614519 | scikit-learn,0.22 |
| 32490 | 1.2835173869971186 | 64.6289348602 | 0.16202688778614519 | numpy,1.19.5 |
| 32490 | 1.2835173869971186 | 64.6289348602 | 0.16202688778614519 | scikit-learn,0.21.3 |
| 32490 | 1.2486163770081475 | 65.273850441 | 0.16202688778614519 | numpy,1.19.5 |
| 32490 | 1.2486163770081475 | 65.273850441 | 0.16202688778614519 | scikit-learn,0.20.3 |
| 32490 | 1.2787502980791032 | 62.6831693649 | 0.16202688778614519 | numpy,1.19.5 |
| 32490 | 1.2787502980791032 | 62.6831693649 | 0.16202688778614519 | scikit-learn,0.19.2 |
