SCRUTINIZE PERFORMANCE COMPARISON OF RANDOM FOREST AND NAIVE BAYES FOR DIFFERENT TEST MODE USING WEKA ON THE DATASET OF CAR REVIEWS
Keywords:Classification, Data mining, Naive Bayes, Random Forest, WEKA.
The amount of data in the world and in our lives seems ever-increasing and there’s no end to it. We are overwhelmed with data. The WWW overwhelms us with information. The Size of information base is increasing day by day with fast speed. The WEKA is data processing tool contain equipped series of state of art machine learning algorithm. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up large-scale experiments on distributed computing platforms, and for designing configurations for streamed data processing. This paper has been carried out to make a performance evaluation of Random Forest from Trees Classifier and Naive Bayes from Bayes Classifier algorithm with different test modes. The test mode used in this research work is Use Training set, 10-folds cross validation. The paper sets out to make comparative evaluation of Random Forest and Naive Bayes in the context of dataset of car reviews to maximize true positive (TP) rate and minimize false positive (FP) rate. The WEKA tools used for result processing. The result in the paper on dataset of car reviews shows that the efficiency and accuracy of Random Forest is excellent as compared to Naive Bayes.
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