Optimization of the Random Forest Algorithm

  • Niva Mohapatra
  • K. ShreyaEmail author
  • Ayes Chinmay
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 37)


Optimization algorithms are implemented for making the field of machine learning more efficient by comparing various solutions until an optimum or a satisfactory answer is found to yield a better accuracy score than the earlier existing one. In this paper, optimization of the Random Forest is performed which is a supervised learning model for classification and regression. A detailed analysis of the optimization technique of this model is done, which follows the unequal weight voting strategy, where weight is assigned based on how well an individual tree performs.


Optimization Supervised Random forest Ensemble Voting 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringInternational Institute of Information Technology BhubaneswarBhubaneswarIndia

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