Date of Award
5-2013
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Mathematics and Statistics
Committee Chair(s)
Adele Cutler
Committee
Adele Cutler
Committee
Yan Sun
Committee
John R. Stevens
Abstract
Random Forests is a useful ensemble approach that provides accurate predictions for classification, regression and many different machine learning problems. Classification has been a very useful and popular application for Random Forests. However, it is preferable to have the probability of a membership rather than the simple knowledge that one belongs to whichever group. Votes and the regression method are current probability estimation methods that have been developed in Random Forests. In this thesis, we introduce two new methods, proximity weighting and the out-of-bag method, trying to improve the current methods. Several different simulations are designed to evaluate the new methods and compare them with the old ones. Finally, we use real data sets from UCI machine learning repository to further evaluate and compare those methods.
Recommended Citation
Li, Chunyang, "Probability Estimation in Random Forests" (2013). All Graduate Plan B and other Reports, Spring 1920 to Spring 2023. 312.
https://digitalcommons.usu.edu/gradreports/312
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