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2015

AutoCompete: A Framework for Machine Learning Competition

Thakur, Abhishek, Krohn-Grimberghe, Artus

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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.

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