Abstract

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning--pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work …

Metadata

publication

Proceedings of the Genetic and Evolutionary Computation Conference 2016, 485-492, 2016

year

2016

publication date

2016/7/20

authors

Randal S Olson, Nathan Bartley, Ryan J Urbanowicz, Jason H Moore

link

https://dl.acm.org/doi/abs/10.1145/2908812.2908918

resource_link

https://dl.acm.org/doi/pdf/10.1145/2908812.2908918

conference

Proceedings of the Genetic and Evolutionary Computation Conference 2016

pages

485-492

publisher

ACM