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.

  • Primer
  • Published:

What are decision trees?

Nature Biotechnology volume 26, pages 1011–1013 (2008)Cite this article

Decision trees have been applied to problems such as assigning protein function and predicting splice sites. How do these classifiers work, what types of problems can they solve and what are their advantages over alternatives?

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: A hypothetical example of how a decision tree might predict protein-protein interactions.

References

  1. Quinlan, J.R. C4.5: Programs for Machine Learning. (Morgan Kaufmann Publishers, San Mateo, CA, USA, 1993).

    Google Scholar 

  2. Breiman, L., Friedman, J., Olshen, R. & Stone, C. Classification and Regression Trees (Wadsworth International Group, Belmont, CA, USA, 1984).

    Google Scholar 

  3. Caruana, R. & Niculescu-Mizil, A. An empirical comparison of supervised learning algorithms. in Machine Learning, Proceedings of the Twenty-Third International Conference (eds. Cohen, W.W. & Moore, A.) 161–168 (ACM, New York, 2003).

    Google Scholar 

  4. Zadrozny, B. & Elkan, C. Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. in Proceedings of the 18th International Conference on Machine Learning, (eds. Brodley, C.E. & Danyluk, A.P.) 609–616 (Morgan Kaufmann, San Francisco, 2001).

    Google Scholar 

  5. Murthy, S.K., Kasif, S. & Salzberg, S. A system for induction of oblique decision trees. J. Artif. Intell. Res. 2, 1–32 (1994).

    Article  Google Scholar 

  6. MacKay, D.J.C. Information Theory, Inference and Learning Algorithms (Cambridge University Press, Cambridge, UK, 2003).

    Google Scholar 

  7. Quinlan, J.R. & Rivest, R.L. Inferring decision trees using the Minimum Description Length Principle. Inf. Comput. 80, 227–248 (1989).

    Article  Google Scholar 

  8. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  9. Heath, D., Kasif, S. & Salzberg, S. Committees of decision trees. in Cognitive Technology: In Search of a Human Interface (eds. Gorayska, B. & Mey, J.) 305–317 (Elsevier Science, Amsterdam, The Netherlands, 1996).

    Chapter  Google Scholar 

  10. Schapire, R.E. The boosting approach to machine learning: an overview. in Nonlinear Estimation and Classification (eds. Denison, D.D., Hansen, M.H., Holmes, C.C., Mallick, B. & Yu, B.) 141–171 (Springer, New York, 2003).

    Google Scholar 

  11. Freund, Y. & Mason, L. The alternating decision tree learning algorithm. in Proceedings of the 16th International Conference on Machine Learning, (eds. Bratko, I. & Džeroski, S.) 124–133 (Morgan Kaufmann, San Francisco, 1999).

    Google Scholar 

  12. Wong, S.L. et al. Combining biological networks to predict genetic interactions. Proc. Natl. Acad. Sci. USA 101, 15682–15687 (2004).

    Article  CAS  Google Scholar 

  13. Allen, J.E., Majoros, W.H., Pertea, M. & Salzberg, S.L. JIGSAW, GeneZilla, and GlimmerHMM: puzzling out the features of human genes in the ENCODE regions. Genome Biol. 7 Suppl, S9 (2006).

  14. Middendorf, M., Kundaje, A., Wiggins, C., Freund, Y. & Leslie, C. Predicting genetic regulatory response using classification. Bioinformatics 20, i232–i240 (2004).

    Article  CAS  Google Scholar 

  15. Chen, X.-W. & Liu, M. Prediction of protein-protein interactions using random decision forest framework. Bioinformatics 21, 4394–4400 (2005).

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Carl Kingsford and Steven L. Salzberg are in the Department of Computer Science, Institute for Advanced Computer Studies and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA. carlk@cs.umd.edu salzberg@umiacs.umd.edu,

    Carl Kingsford & Steven L Salzberg

Authors
  1. Carl Kingsford
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Steven L Salzberg
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kingsford, C., Salzberg, S. What are decision trees?. Nat Biotechnol 26, 1011–1013 (2008). https://doi.org/10.1038/nbt0908-1011

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt0908-1011

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