Supervised Learning Techniques : A comparison of the Random Forest and the Support Vector Machine
This thesis examines the performance of the support vector machine and the random forest models in the context of binary classification. The two techniques are compared and the outstanding one is used to construct a final parsimonious model. The data set consists of 33 observations and 89 biomarkers...
| Main Authors: | , |
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| Format: | Others |
| Language: | English |
| Published: |
Uppsala universitet, Statistiska institutionen
2016
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| Subjects: | |
| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-274768 |