Visualizing Bagged Decision Trees. Rao, J S. & Potts, W. J E abstract bibtex We present a visual tablet for exploring the nature of a bagged decision tree (Breiman [1996]). Aggregating classifiers over bootstrap datasets (bagging) can result in greatly improved prediction accuracy. Bagging is motivated as a variance reduction technique, but it is considered a black box with respect to interpretation. Current research seekine: to explain why bagging works has focused ondifferent bias/variance decompositions of prediction error. We show that bagging’s complexity can be better understood by a simple graphical technique that allows visualizing the bagged decision boundary in low-dimensional situations. We then show that bagging can be heuristically motivated as a method to enhance local adaptivity of the boundary. Some simulated examples are presented to illustrate the technique.
@article{rao_visualizing_nodate,
title = {Visualizing {Bagged} {Decision} {Trees}},
abstract = {We present a visual tablet for exploring the nature of a bagged decision tree (Breiman [1996]). Aggregating classifiers over bootstrap datasets (bagging) can result in greatly improved prediction accuracy. Bagging is motivated as a variance reduction technique, but it is considered a black box with respect to interpretation. Current research seekine: to explain why bagging works has focused ondifferent bias/variance decompositions of prediction error. We show that bagging’s complexity can be better understood by a simple graphical technique that allows visualizing the bagged decision boundary in low-dimensional situations. We then show that bagging can be heuristically motivated as a method to enhance local adaptivity of the boundary. Some simulated examples are presented to illustrate the technique.},
language = {en},
author = {Rao, J Sunil and Potts, William J E},
pages = {3}
}
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