Consistency of Online Random Forests
Misha Denil, David Matheson, Nando de Freitas
TL;DR
This paper establishes a theoretical consistency result for online random forests, bridging the gap between their practical success and theoretical understanding.
Contribution
It provides the first known consistency proof for online random forests, advancing the theoretical foundation of this widely used machine learning method.
Findings
Online random forests are proven to be consistent.
Theoretical guarantees support practical effectiveness.
Bridges gap between theory and application.
Abstract
As a testament to their success, the theory of random forests has long been outpaced by their application in practice. In this paper, we take a step towards narrowing this gap by providing a consistency result for online random forests.
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Taxonomy
TopicsData Stream Mining Techniques · Data Management and Algorithms · Machine Learning and Data Classification