Artificial intelligence-assisted data analysis with BayesDB
Name
1066344990-MIT.pdf
Description
Full printable version
Size
1.98 MB
Format
Adobe PDF
Checksum (MD5)
71254103cc126fb28cf835b7fd4cf978
Author(s)
Curlette, Christina M
Advisor(s)
Vikash K. Mansinghka.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
When applying machine learning and statistics techniques to real-world datasets, problems often arise due to missing data or errors from black-box predictive models that are difficult to understand or explain in terms of the model's inputs. This thesis explores the applicability of BayesDB, a probabilistic programming platform for data analysis, to three common problems in data analysis: (i) modeling patterns of missing data, (ii) imputing missing values in datasets, and (iii) characterizing the error behavior of predictive models. Experiments show that CrossCat, the default model discovery mechanism used by BayesDB, can address all three problems effectively. Examples are drawn from the American National Election Studies and the Gapminder database of global macroeconomic and public health indicators.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
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 (pages 67-68).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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