AutoCompete: A Framework for Machine Learning Competition
This work addresses the need for reduced human intervention in machine learning competitions, but it appears incremental as it builds on existing automation concepts.
The authors tackled the problem of automating machine learning competition pipelines by proposing AutoCompete, a framework that minimizes human effort in building predictive models and assessing challenge difficulty, resulting in better or comparable performance with less runtime compared to other approaches.
In this paper, we propose AutoCompete, a highly automated machine learning framework for tackling machine learning competitions. This framework has been learned by us, validated and improved over a period of more than two years by participating in online machine learning competitions. It aims at minimizing human interference required to build a first useful predictive model and to assess the practical difficulty of a given machine learning challenge. The proposed system helps in identifying data types, choosing a machine learn- ing model, tuning hyper-parameters, avoiding over-fitting and optimization for a provided evaluation metric. We also observe that the proposed system produces better (or comparable) results with less runtime as compared to other approaches.