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dc.contributor.advisorSam Madden.en_US
dc.contributor.authorTromba, Isabella Men_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2018-12-18T19:46:33Z
dc.date.available2018-12-18T19:46:33Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/119705
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 61-64).en_US
dc.description.abstractMakeML is a software system that enables knowledge workers with no programming experience to easily and quickly create machine learning models that have competitive performance with models hand-built by trained data scientists. MakeML consists of a web-based application similar to a spreadsheet in which users select features and choose a target column to predict. MakeML then automates the process of feature engineering, model selection, training, and hyperparameter optimization. After training, the user can evaluate the performance of the model and can make predictions on new data using the web interface. We show that a model generated automatically using MakeML is able to achieve accuracy better than 90% of submissions for the Titanic problem on the public data science platform Kaggle.en_US
dc.description.statementofresponsibilityby Isabella M. Tromba.en_US
dc.format.extent64 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleMakeML : automated machine learning from data to predictionsen_US
dc.title.alternativeAutomated machine learning from data to predictionsen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc1078154256en_US


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