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A Review on Random Forest: An Ensemble Classifier

  • Aakash Parmar
  • Rakesh Katariya
  • Vatsal Patel
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)

Abstract

Ensemble classification is an information mining approach which utilizes various classifiers that cooperate for distinguishing the class label for new unlabeled thing from accumulation. Arbitrary Forest approach joins a few randomized choice trees and totals their forecasts by averaging. It has grabbed well-known attention from the community of research because of its high accuracy and superiority which additionally increase the performance. Now in this paper, we take a gander at improvements of Random Forest from history to till date. Our approach is to take a recorded view on the improvement of this prominently effective classification procedure. To begin with history of Random Forest to main technique proposed by Breiman then successful applications that utilized Random Forest and finally some comparison with other classifiers. This paper is proposed to give non specialists simple access to the principle thoughts of random forest.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.SS Agrawal Institue of Engineering and TechnologyNavsariIndia

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