  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.
