Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool

@article{Olson2016IdentifyingAH,
  title={Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool},
  author={Randal S. Olson and Jason H. Moore},
  journal={ArXiv},
  year={2016},
  volume={abs/1607.08878}
}
As data science continues to grow in popularity, there will be an increasing need to make data science tools more scalable, flexible, and accessible. [] Key Method Further, we analyze a large database of pipelines that were previously used to solve various supervised classification problems and identify 100 short series of machine learning operations that appear the most frequently, which we call the building blocks of machine learning pipelines. We harness these building blocks to initialize TPOT with…

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