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Comparative Analysis of Decision Tree Algorithms: ID3, C4.5 and Random Forest

  • Shiju Sathyadevan
  • Remya R. Nair
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)

Abstract

To analyze the raw data manually and find the correct information from it is a tough process. But Data mining technique automatically detect the relevant patterns or information from the raw data, using the data mining algorithms. In Data mining algorithms, Decision trees are the best and commonly used approach for representing the data. Using these Decision trees, data can be represented as a most visualizing form. Many different decision tree algorithms are used for the data mining technique. Each algorithm gives a unique decision tree from the input data. This paper focus on the comparison of different decision tree algorithms for data analysis.

Keywords

Iterative dichotomiser 3 (ID3) C4.5 Randomforest 

Notes

Acknowledgments

We thank our college Amrita School of Engineering, Amritapuri and Amrita Center of Cyber Security, Amritapuri for giving us an opportunity to be a part of the internship program that leads to the development of this work. Many thanks to Shiju Sathyadevan for countless discussions and feedback that help me to complete the work successfully.

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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Amrita Center for Cyber Security Systems and NetworksAmrita UniversityKollamIndia

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