Incorporating automated feature engineering routines into automated machine learning pipelines
Author(s)
Runnels, Wesley(Wesley J.)
Download1193029100-MIT.pdf (660.6Kb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Tim Kraska.
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Metadata
Show full item recordAbstract
Automating the construction of consistently high-performing machine learning pipelines has remained difficult for researchers, especially given the domain knowledge and expertise often necessary for achieving optimal performance on a given dataset. In particular, the task of feature engineering, a key step in achieving high performance for machine learning tasks, is still mostly performed manually by experienced data scientists. In this thesis, building upon the results of prior work in this domain, we present a tool, rl_feature_eng, which automatically generates promising features for an arbitrary dataset. In particular, this tool is specically adapted to the requirements of augmenting a more general auto-ML framework. We discuss the performance of this tool in a series of experiments highlighting the various options available for use, and finally discuss its performance when used in conjunction with Alpine Meadow, a general auto-ML package.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020 Cataloged from the official PDF of thesis. Includes bibliographical references (pages 47-48).
Date issued
2020Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.