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An efficient approach to pruning regression trees using a modified Bayesian information criterion

Thesis type
(Project) M.Sc.
Date created
By identifying relationships between regression tree construction and change-point detection, we show that it is possible to prune a regression tree efficiently using properly modified information criteria. We prove that one of the proposed pruning approaches that uses a modified Bayesian information criterion consistently recovers the true tree structure provided that the true regression function can be represented as a subtree of a full tree. In practice, we obtain simplified trees that can have prediction accuracy comparable to trees obtained using standard cost-complexity pruning. We briefly discuss an extension to random forests that prunes trees adaptively in order to prevent excessive variance, building upon the work of other authors.
Copyright statement
Copyright is held by the author(s).
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Loughin, Thomas M.
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