Comparing the Performance of Naive Bayes And Decision Tree Classification Using R
Автор: Kirtika Yadav, Reema Thareja
Журнал: International Journal of Intelligent Systems and Applications @ijisa
Статья в выпуске: 12 vol.11, 2019 года.
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The use of technology is at its peak. Many companies try to reduce the work and get an efficient result in a specific amount of time. But a large amount of data is being processed each day that is being stored and turned into large datasets. To get useful information, the dataset needs to be analyzed so that one can extract knowledge by training the machine. Thus, it is important to analyze and extract knowledge from a large dataset. In this paper, we have used two popular classification techniques- Decision tree and Naive Bayes to compare the performance of the classification of our data set. We have taken student performance dataset that has 480 observations. We have classified these students into different groups and then calculated the accuracy of our classification by using the R language. Decision tree uses a divide and conquer method including some rules that makes it easy for humans to understand. The Naive Bayes theorem includes an assumption that the pair of features being classified are independent. It is based on the Bayes theorem.
Decision tree classification, Data mining, R language, Supervised learning, Naive Bayes classification
Короткий адрес: https://sciup.org/15017113
IDR: 15017113 | DOI: 10.5815/ijisa.2019.12.02
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