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. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/23649 |
| Date | January 2018 |
| Creators | Le Fort, Eric |
| Contributors | Deza, Antoine, Franek, Frantisek, Computing and Software |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0022 seconds