MLFeb 20, 2013

Consistency of Online Random Forests

arXiv:1302.4853v293 citations
AI Analysis

This addresses the lack of theoretical guarantees for online random forests, which is incremental as it builds on existing random forest theory.

The paper tackles the theoretical gap in random forests by proving consistency for online random forests, providing a formal result that aligns with their practical success.

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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Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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