TEST

, Volume 25, Issue 2, pp 254–260

Comments on: A random forest guided tour

Discussion

DOI: 10.1007/s11749-016-0485-3

Cite this article as:
Hooker, G. & Mentch, L. TEST (2016) 25: 254. doi:10.1007/s11749-016-0485-3
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Abstract

We discuss future challenges in developing statistical theory for Random Forests. In particular, we suggest that an analysis of bias and extrapolation is vital to understanding the statistical properties of variable importance measures. We further point to the incorporation of random forests within larger statistical models as an important tool for high-dimensional statistical inference.

Keywords

Random forests Machine learning Extrapolation Variable importance 

Mathematics Subject Classification

62G09 

Copyright information

© Sociedad de Estadística e Investigación Operativa 2016

Authors and Affiliations

  1. 1.Department of Biological Statistics and Computational BiologyCornell UniversityIthacaUSA
  2. 2.Department of StatisticsUniversity of PittsburghPittsburghUSA

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