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.
@article{arxiv.1507.02188,
title = {AutoCompete: A Framework for Machine Learning Competition},
author = {Abhishek Thakur and Artus Krohn-Grimberghe},
journal= {arXiv preprint arXiv:1507.02188},
year = {2015}
}