A Comparative Study of Machine Learning Algorithms
| dc.contributor.advisor | Deza, Antoine | |
| dc.contributor.advisor | Franek, Frantisek | |
| dc.contributor.author | Le Fort, Eric | |
| dc.contributor.department | Computing and Software | en_US |
| dc.date.accessioned | 2018-12-14T21:08:40Z | |
| dc.date.available | 2018-12-14T21:08:40Z | |
| dc.date.issued | 2018 | |
| dc.description.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. | en_US |
| dc.description.degree | Master of Applied Science (MASc) | en_US |
| dc.description.degreetype | Thesis | en_US |
| dc.identifier.uri | http://hdl.handle.net/11375/23649 | |
| dc.language.iso | en | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Comparative Study | en_US |
| dc.subject | Data Science | en_US |
| dc.subject | University Admissions | en_US |
| dc.subject | Software Engineering | en_US |
| dc.subject | Computer Science | en_US |
| dc.subject | K-Nearest Neighbours | en_US |
| dc.subject | Decision Tree | en_US |
| dc.subject | Random Forest | en_US |
| dc.subject | Gradient Tree Boosting | en_US |
| dc.subject | Logistic Regression | en_US |
| dc.subject | Naive Bayes | en_US |
| dc.subject | Support Vector Machine | en_US |
| dc.subject | Neural Network | en_US |
| dc.title | A Comparative Study of Machine Learning Algorithms | en_US |
| dc.type | Thesis | en_US |