Corpus ID: 233878452

Ease.ml/snoopy: Towards Automatic Feasibility Study for Machine Learning Applications

@inproceedings{Renggli2020EasemlsnoopyTA,
  title={Ease.ml/snoopy: Towards Automatic Feasibility Study for Machine Learning Applications},
  author={C{\'e}dric Renggli and Luka Rimanic and Luka Kolar and Nora Hollenstein and Wentao Wu and Ce Zhang},
  year={2020}
}
In our experience of working with domain experts who are using today's AutoML systems, a common problem we encountered is what we call"unrealistic expectations"-- when users are facing a very challenging task with noisy data acquisition process, whilst being expected to achieve startlingly high accuracy with machine learning (ML). Consequently, many computationally expensive AutoML runs and labour-intensive ML development processes are predestined to fail from the beginning. In traditional… Expand