# AutoCompete: A Framework for Machine Learning Competition

arXiv: https://arxiv.org/abs/1507.02188 · HTML: https://mlsanity.com/paper/1507.02188/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2015.

## Built on by (most cited first, 2 of 2)

- [CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System](https://mlsanity.com/paper/2405.14691/) (2024)
- [AutoTrain: No-code training for state-of-the-art models](https://mlsanity.com/paper/2410.15735/) (2024)

## Abstract (arXiv)

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.