• Corpus ID: 52105215

P4ML: A Phased Performance-Based Pipeline Planner for Automated Machine Learning

@inproceedings{Gil2018P4MLAP,
  title={P4ML: A Phased Performance-Based Pipeline Planner for Automated Machine Learning},
  author={Yolanda Gil and Ke-Thia Yao and Varun Ratnakar and Daniel Garijo and Greg Ver Steeg and Rob Brekelmans and Mayank Kejriwal and Fanghao Luo and I-Hui Huang},
  year={2018}
}
While many problems could benefit from recent advances in machine learning, significant time and expertise are required to design customized solutions to each problem. Prior attempts to automate machine learning have focused on generating multi-step solutions composed of primitive steps for feature engineering and modeling, but using already clean and featurized data and carefully curated primitives. However, cleaning and featurization are often the most time-consuming steps in a data science… 

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