Open AccessAnalysis of Decision Tree Induction Algorithms
Author(s) -
Hugo Kenji Rodrigues Okada,
André Ricardo Nascimento das Neves,
Ricardo Shitsuka
Publication year - 2019
Publication title -
research, society and development
Language(s) - English
Resource type - Journals
ISSN - 2525-3409
DOI - 10.33448/rsd-v8i11.1473
Subject(s) - decision tree , cart , decision tree learning , computer science , machine learning , alternating decision tree , id3 algorithm , incremental decision tree , tree (set theory) , artificial intelligence , rule induction , decision tree model , nonparametric statistics , algorithm , data mining , mathematics , statistics , engineering , mechanical engineering , mathematical analysis
Decision trees are data structures or computational methods that enable nonparametric supervised machine learning and are used in classification and regression tasks. The aim of this paper is to present a comparison between the decision tree induction algorithms C4.5 and CART. A quantitative study is performed in which the two methods are compared by analyzing the following aspects: operation and complexity. The experiments presented practically equal hit percentages in the execution time for tree induction, however, the CART algorithm was approximately 46.24% slower than C4.5 and was considered to be more effective.