Automl On Madeline
평가 지표
Duration
Rank (AutoML5)
Set1 (F1)
Set2 (PAC)
Set3 (AUC)
Set4 (ABS)
Set5 (BAC)
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | Duration | Rank (AutoML5) | Set1 (F1) | Set2 (PAC) | Set3 (AUC) | Set4 (ABS) | Set5 (BAC) | Paper Title | Repository |
|---|---|---|---|---|---|---|---|---|---|
| djajetic | 5842.12 | 3.00 | 0.7531 | 0.3905 | 0.6875 | 0.3067 | 0.5517 | Analysis of the AutoML Challenge Series 2015–2018 | - |
| aad_freiburg | 5942.22 | 1.60 | 0.7947 | 0.4061 | 0.5543 | 0.2957 | 0.5900 | Analysis of the AutoML Challenge Series 2015–2018 | - |
| postech.mlg_exbrain | 3343.64 | 5.20 | 0.7542 | 0.2802 | 0.3333 | 0.1507 | 0.5564 | Analysis of the AutoML Challenge Series 2015–2018 | - |
| abhishek4 | 4353.45 | 4.60 | 0.7565 | 0.0172 | 0.2911 | 0.2791 | 0.5595 | Analysis of the AutoML Challenge Series 2015–2018 | - |
| reference_mb | 4889.14 | 5.20 | 0.7005 | 0.3698 | 0.6776 | 0.2507 | 0.4618 | Analysis of the AutoML Challenge Series 2015–2018 | - |
| marc.boulle | 4603.81 | 6.40 | 0.7005 | 0.3698 | -1.0000 | 0.2507 | 0.4618 | Analysis of the AutoML Challenge Series 2015–2018 | - |
| reference | 4416.40 | 4.40 | 0.7556 | 0.0343 | 0.2927 | 0.2790 | 0.5601 | Analysis of the AutoML Challenge Series 2015–2018 | - |
| reference_ls | 5879.88 | 4.00 | 0.7062 | 0.3708 | 0.5384 | 0.2856 | 0.5580 | Analysis of the AutoML Challenge Series 2015–2018 | - |
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