A framework to search for machine learning pipelines

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Ravikumar, Akshay

Advisor dc:contributor.advisor

• Kalyan Veeramachaneni.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119720

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119720