A framework to search for machine learning pipelines
Name
1078638316-MIT.pdf
Description
Full printable version
Size
834.29 KB
Format
Adobe PDF
Checksum (MD5)
3eb94e2d4daf0d997b43491c49d15d87
Author(s)
Ravikumar, Akshay
Advisor(s)
Kalyan Veeramachaneni.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we present DeepMining, a framework to search for machine learning pipelines. The high-level goal of DeepMining is to solve the pipeline search problem: given a problem and a dataset, find the pipeline best-suited to solve that problem. The DeepMining platform serves as a testbed for developers to experiment with different methods of computing and evaluating machine learning pipelines. Specifically, developers have autonomy over how to evaluate different configurations in parallel, score a pipeline given a dataset and hyperparameter configuration, and efficiently search over the pipeline space. DeepMining was designed with modularity and extensibility in mind: developers can easily implement new search algorithms, scoring functions, and computation frameworks. At the same time, users can switch between these modules with minimal effort.
Description
Thesis: M. Eng. in Computer Science, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 81).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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