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This essay investigates the question of how the naive Bayes classifier andthe support vector machine compare in their ability to forecast the StockExchange of Thailand. The theory behind the SVM and the naive Bayes classifieris explored. The algorithms are trained using data from the month of January2010, extracted from the MarketWatch.com website. Input features are selectedbased on previous studies of the SET100 Index. The Weka 3 software is used tocreate models from the labeled training data. Mean squared error and proportionof correctly classified instances, and a number of other error measurements arethe used to compare the two algorithms. This essay shows that these twoalgorithms are currently not advanced enough to accurately model the stockexchange. Nevertheless, the naive Bayes is better than the support vectormachine at predicting the Stock Exchange of Thailand.

How do the naive Bayes classifier and the Support Vector Machine compare in their ability to forecast the Stock Exchange of Thailand?