Estimating the Operating Characteristics of Ensemble Methods

Anthony Gamst; Jay-Calvin Reyes; Alden Walker

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arXiv · 1710.08952

Estimating the Operating Characteristics of Ensemble Methods

Anthony Gamst·Jay-Calvin Reyes·Alden Walker

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Abstract

In this paper we present a technique for using the bootstrap to estimate the operating characteristics and their variability for certain types of ensemble methods. Bootstrapping a model can require a huge amount of work if the training data set is large. Fortunately in many cases the technique lets us determine the effect of infinite resampling without actually refitting a single model. We apply the technique to the study of meta-parameter selection for random forests. We demonstrate that alternatives to bootstrap aggregation and to considering \sqrt{d} features to split each node, where d is the number of features, can produce improvements in predictive accuracy.

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Anthony Gamst, Jay-Calvin Reyes, Alden Walker. 2017-10-24. Estimating the Operating Characteristics of Ensemble Methods. https://arxiv.org/abs/1710.08952

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