Comments on: A random forest guided tour

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.

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Acknowledgments

This work was supported by NSF grants DMS-103252 and DEB-1353039.

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Correspondence to Giles Hooker.

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This comment refers to the invited paper available at: doi:10.1007/s11749-016-0481-7.

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Hooker, G., Mentch, L. Comments on: A random forest guided tour . TEST 25, 254–260 (2016). https://doi.org/10.1007/s11749-016-0485-3

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Keywords

  • Random forests
  • Machine learning
  • Extrapolation
  • Variable importance

Mathematics Subject Classification

  • 62G09