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University of Toledo

1. Park, Samuel M. A Comparison of Machine Learning Techniques to Predict University Rates.

Degree: MS, Mathematics, 2019, University of Toledo

In recent years the use of machine learning techniques in data analysis has grown immensely in popularity. While the use of such techniques has been helpful for those interested in data analytics, it is important to understand the underlying structures of these methods in order to implement them more effectively. In this thesis we will discuss the motivation behind decision trees, random forests, support vector machines, and neural networks, alongside the more traditional logistic regression and the Generalized Additive Partially Linear Model (GAPLM) estimator developed by Liu et al. We will also discuss cross validation as well as ROC and AUC as ways to compare the effectiveness between these models. We conclude this thesis with an application of these methods by predicting whether or not an undergraduate student, who is enrolled in the fall semester, will enroll in the following spring semester. We also include Linear Discriminant Analysis and Quadratic Discriminant Analysis in the data analysis portion of this thesis. We find that the GAPLM method performs the best out of all the methods used. Advisors/Committee Members: Liu, Rong (Committee Chair).

Subjects/Keywords: Mathematics; Statistics; Machine Learning, Neural Network, Decision Trees, Random Forest, Support Vector Machines, GAPLM, University, Retention, Rates

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Park, S. M. (2019). A Comparison of Machine Learning Techniques to Predict University Rates. (Masters Thesis). University of Toledo. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=toledo1564790014887692

Chicago Manual of Style (16th Edition):

Park, Samuel M. “A Comparison of Machine Learning Techniques to Predict University Rates.” 2019. Masters Thesis, University of Toledo. Accessed September 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1564790014887692.

MLA Handbook (7th Edition):

Park, Samuel M. “A Comparison of Machine Learning Techniques to Predict University Rates.” 2019. Web. 21 Sep 2019.

Vancouver:

Park SM. A Comparison of Machine Learning Techniques to Predict University Rates. [Internet] [Masters thesis]. University of Toledo; 2019. [cited 2019 Sep 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1564790014887692.

Council of Science Editors:

Park SM. A Comparison of Machine Learning Techniques to Predict University Rates. [Masters Thesis]. University of Toledo; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1564790014887692

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