A naÏve Bayes classifier trained with 1,360 samples from 17 volunteers performs at an accuracy of 72.5% (based on 10-fold cross validation). This accuracy is based on using the entire data set. One approach to increasing the accuracy is by analyzing the data and removing irregular samples from the training set. As the quality of the training data increases, the accuracy of the classifier will increase. This report describes analysis of 2 features of the training data, observed unusual patterns, and how fine tuning the training set increased the accuracy by 7.2%.
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