Model factory : a new way to look at data through models
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2017
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| Online Access: | http://hdl.handle.net/1721.1/106392 |
| _version_ | 1826218091080581120 |
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| author | Wu, Yonglin, M. Eng Massachusetts Institute of Technology |
| author2 | Kalyan Veeramachaneni. |
| author_facet | Kalyan Veeramachaneni. Wu, Yonglin, M. Eng Massachusetts Institute of Technology |
| author_sort | Wu, Yonglin, M. Eng Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016. |
| first_indexed | 2024-09-23T17:14:01Z |
| format | Thesis |
| id | mit-1721.1/106392 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T17:14:01Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1063922019-04-10T16:00:08Z Model factory : a new way to look at data through models Wu, Yonglin, M. Eng Massachusetts Institute of Technology Kalyan Veeramachaneni. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016. 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 67). In this thesis, we present Model Factory, a software framework that is able to generate predictive models from raw data. We present two foundational representations for data: an event-driven time series and a feature series. Together, they allow us to define a large suite of predictive modeling problems, and to subsequently solve them. We applied Model Factory to two real world datasets: one made up of sensor recordings from prototype cars, and the other containing time-varying status values for projects managed by a consulting firm. We deployed Model Factory on each of these datasets. Through the framework, we were able to enumerate a total of 3,877,848 predictive problems for the car dataset and 125,028 for the project dataset. We randomly sampled 150 and 1,000 prediction problems from the two datasets respectively, and solved them using off-the-shelf machine learning algorithms. We demonstrated our ability to build models for these prediction problems, and to gain insights into the data. We also built a graphical user interface on top of Model Factory for less tech-savvy users. by Yonglin Wu. M. Eng. 2017-01-12T18:18:55Z 2017-01-12T18:18:55Z 2016 2016 Thesis http://hdl.handle.net/1721.1/106392 967666287 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 67 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Wu, Yonglin, M. Eng Massachusetts Institute of Technology Model factory : a new way to look at data through models |
| title | Model factory : a new way to look at data through models |
| title_full | Model factory : a new way to look at data through models |
| title_fullStr | Model factory : a new way to look at data through models |
| title_full_unstemmed | Model factory : a new way to look at data through models |
| title_short | Model factory : a new way to look at data through models |
| title_sort | model factory a new way to look at data through models |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/106392 |
| work_keys_str_mv | AT wuyonglinmengmassachusettsinstituteoftechnology modelfactoryanewwaytolookatdatathroughmodels |