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Analog the Performance between Three Classifiers on Bank Marketing Data
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1066.0782s319
Subject(s) - naive bayes classifier , computer science , classifier (uml) , artificial intelligence , decision tree , machine learning , bayes error rate , bayes classifier , artificial neural network , pattern recognition (psychology) , data mining , support vector machine
There are several different classification methods can be used to do the classification which can classified the data into specified groups or classes. This paper presents a comparison of performance between three classifiers which include Naïve Bayes, Decision Tree and Neural Network on Bank Marketing dataset. This study focus on which classifier will have the better performance based on some performance measure in two different datasets. The result shows that machine learning classifier was not able compare to Naïve Bayes and Decision Tree classifier. Based on the results, the huge dataset obtained the more information which can be predict accurately and identify the performance of the classifier correctly.

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