I'm using Naive Bayes classifier in Weka on a data set of 7000 instances with 15 attributes. My baseline accuracy is 87.5% using ZeroR. As a part of data preprocessing I normalized the data set with zero mean and unit variance, applied filter to randomize the dataset. I've used training (70%) and testing (30%) sets, as well as 10-fold cross validation on the entire data set, used supervised discretization and attribute selection and the best accuracy of the classifier I got is 93.43%. Is this small improvement in respect to baseline accuracy?
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