Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/23649
Title: | A Comparative Study of Machine Learning Algorithms |
Authors: | Le Fort, Eric |
Advisor: | Deza, Antoine Franek, Frantisek |
Department: | Computing and Software |
Keywords: | Machine Learning;Comparative Study;Data Science;University Admissions;Software Engineering;Computer Science;K-Nearest Neighbours;Decision Tree;Random Forest;Gradient Tree Boosting;Logistic Regression;Naive Bayes;Support Vector Machine;Neural Network |
Publication Date: | 2018 |
Abstract: | The selection of machine learning algorithm used to solve a problem is an important choice. This paper outlines research measuring three performance metrics for eight different algorithms on a prediction task involving under- graduate admissions data. The algorithms that were tested are k-nearest neighbours, decision trees, random forests, gradient tree boosting, logistic regression, naive bayes, support vector machines, and artificial neural net- works. These algorithms were compared in terms of accuracy, training time, and execution time. |
URI: | http://hdl.handle.net/11375/23649 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Le_Fort_Eric_L_August2018_MASC.pdf | 516.63 kB | Adobe PDF | View/Open |
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