Incorporating automated feature engineering routines into automated machine learning pipelines
Name
1193029100-MIT.pdf
Size
660.61 KB
Format
Adobe PDF
Checksum (MD5)
01e47cb6e84500a27a4c9295ca870e9c
Author(s)
Runnels, Wesley(Wesley J.)
Advisor(s)
Tim Kraska.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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