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arXiv · 1507.02188

AutoCompete: A Framework for Machine Learning Competition

Abhishek Thakur·Artus Krohn-Grimberghe
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Abstract

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.

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Abhishek Thakur, Artus Krohn-Grimberghe. 2015-07-08. AutoCompete: A Framework for Machine Learning Competition. https://arxiv.org/abs/1507.02188

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