NAIVE BAYES CLASSIFIER, DECISION TREE AND ADABOOST ENSEMBLE ALGORITHM – ADVANTAGES AND DISADVANTAGES
NAIVE BAYES CLASSIFIER, DECISION TREE AND ADABOOST ENSEMBLE ALGORITHM – ADVANTAGES AND DISADVANTAGES

Author(s): Neli Kalcheva, Maya Todorova, Ginka Marinova
Subject(s): Social Sciences, Business Economy / Management, ICT Information and Communications Technologies
Published by: Udruženje ekonomista i menadžera Balkana
Keywords: Classification; Machine learning; Naive Bayes classifier; Decision tree; Ada Boost Ensemble algorithm.
Summary/Abstract: The purpose of the publication is to analyse popular classification algorithms in machine learning. The following classifiers were studied: Naive Bayes Classifier, Decision Tree and AdaBoost Ensemble Algorithm. Their advantages and disadvantages are discussed. Research shows that there is no comprehensive universal method or algorithm for classification in machine learning. Each method or algorithm works well depending on the specifics of the task and the data used.

• Details
• Contents