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Random Forests

  • Gopinath Rebala
  • Ajay Ravi
  • Sanjay Churiwala
Chapter

Abstract

Random Forests are effective and intuitive models used in classification and regression problems. They are intuitive because they provide clear path to a result and are based on underlying Decision Tree structures. A Decision Tree is a machine learning model built using series of decisions based on variable values to take one path or the other. A Random Forest is a collection of Decision Trees that improve the prediction over a single Decision Tree.

Bibliography

  1. Breiman L (2001) Random forests. Mach Learn J 45(1):5–32CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gopinath Rebala
    • 1
  • Ajay Ravi
    • 2
  • Sanjay Churiwala
    • 3
  1. 1.OpsMx IncSan RamonUSA
  2. 2.San JoseUSA
  3. 3.HyderabadIndia

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