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A Comparison of Machine Learning Tec...

Park, Samuel.

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A Comparison of Machine Learning Techniques to Predict University Rates.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Comparison of Machine Learning Techniques to Predict University Rates./
作者:	Park, Samuel.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	49 p.
附註:	Source: Masters Abstracts International, Volume: 81-06.
Contained By:	Masters Abstracts International81-06.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27603035
ISBN:	9781687976871

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

A Comparison of Machine Learning Techniques to Predict University Rates. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 49 p.

Source: Masters Abstracts International, Volume: 81-06.

Thesis (M.S.)--The University of Toledo, 2019.

This item must not be sold to any third party vendors.

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.

ISBN: 9781687976871Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Machine Learning

A Comparison of Machine Learning Techniques to Predict University Rates.
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