Thanks to visit codestin.com
Credit goes to www.nature.com

Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • This Month
  • Published:

Points of Significance

Ensemble methods: bagging and random forests

Nature Methods volume 14, pages 933–934 (2017)Cite this article

Subjects

Many heads are better than one.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Bagging applied to regression using a regression tree.
Figure 2: Ensemble regressions improve in quality, up to a point, as the number of bootstraps is increased.
Figure 3: Application of bagging to classification using a decision tree applied to n = 100 two-dimensional data points assigned to one of four color categories.

References

  1. Kulesa, A., Krzywinski, M., Blainey, P. & Altman, N. Nat. Methods 12, 477–478 (2015).

    Article  CAS  Google Scholar 

  2. Lever, J., Krzywinski, M. & Altman, N. Nat. Methods 13, 541–542 (2016).

    Article  CAS  Google Scholar 

  3. Breiman, L. Mach. Learn. 24, 123–140 (1996).

    Google Scholar 

  4. Krzywinski, M. & Altman, N. Nat. Methods 14, 757–758 (2017).

    Article  CAS  Google Scholar 

  5. Lever, J., Krzywinski, M. & Altman, N. Nat. Methods 13, 703–704 (2016).

    Article  CAS  Google Scholar 

  6. Liang, G., Zhu, X. & Zhang, C. in Proc. 25th AAAI Conference on Artificial Intelligence (eds. Wang, D. & Reynolds, M.) 1802–1803 (Springer, 2011).

    Google Scholar 

  7. Breiman, L. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Professor of Statistics at The Pennsylvania State University,

    Naomi Altman

  2. staff scientist at Canada's Michael Smith Genome Sciences Centre,

    Martin Krzywinski

Authors
  1. Naomi Altman
    View author publications

    Search author on:PubMed Google Scholar

  2. Martin Krzywinski
    View author publications

    Search author on:PubMed Google Scholar

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Altman, N., Krzywinski, M. Ensemble methods: bagging and random forests. Nat Methods 14, 933–934 (2017). https://doi.org/10.1038/nmeth.4438

Download citation

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/nmeth.4438

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing