Decision Trees for Statistical Learning

Host Faculty: Engineering
General Subject Area: Statistics and Data Science
Project Level: PhD
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Classification problems occur in many fields including statistics, data science, machine learning and medicine. The purpose of a classification algorithm is to assign a class label, or the probability of being in a particular class, to an unclassified example. Decision tree classifiers are conceptually simple, making them a popular statistical learning method. This project considers using oblique decision trees in ensemble learning, where the ensemble is built using techniques from random forest and boosting. The proposed methods will be numerically tested on a wide range of datasets.


Supervisor: Blair Robertson

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Algorithms; statistical learning; data science