This paper is published in Volume-4, Issue-3, 2018
Area
Computer Science
Author
Nikita Sandhu
Org/Univ
Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India
Pub. Date
12 June, 2018
Paper ID
V4I3-1844
Publisher
Keywords
Classification, RainForest, Decision tree

Citationsacebook

IEEE
Nikita Sandhu. RainForest Framework: A Recent Review, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Nikita Sandhu (2018). RainForest Framework: A Recent Review. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.

MLA
Nikita Sandhu. "RainForest Framework: A Recent Review." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.

Abstract

Machine learning uses a decision tree to describe data like, from the observation of tuples to tuple’s target value. The decision tree is a type of a classification technique where the target outcome is the class to which the data belongs. Classification problem is among the major data mining problems. Numerous classification algorithms have been launched although there was hardly an algorithm that surpasses all distinct algorithms with regard to standard. RainForest framework deals with the issue of scalability and it has different types of algorithms that work under different types of cases. In this paper, a brief review of Rainforest framework is provided that overcomes the limitations of scalability in the construction of Decision Tree.