Date of Award
Master of Science (MS)
Mathematics and Statistics
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.
Li, Chunyang, "Probability Estimation in Random Forests" (2013). All Graduate Plan B and other Reports. 312.
Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .