Approximate cross validation for sparse generalized linear models
Author(s)Stephenson, William T.(William Thomas)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
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Cross validation (CV) is an effective yet computationally expensive tool for assessing the out of sample error for many methods in machine learning and statistics. Previous work has shown that methods to approximate CV can be very accurate and computationally cheap, but only for low dimensional problems. In this thesis, a modification of existing methods is developed to extend the high accuracy of these techniques to high dimensional settings.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from PDF version of thesis.Includes bibliographical references (pages 59-60).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.