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Theres a very important thing that I was keeping an eye on as I was drawing up my training set of all the persons of interest, and that is basically the size of that training set. The reason that the size of your training set is so important is that it usually has a big effect on the accuracy that you are able to achieve with your classifier. Let me show you an example of what I mean. I once had a problem that I was trying to solve in physics. I was using a naive Bayes classifier to try to identify certain types of particles, and I had 1,000 events of each class in my training set. And my question was basically, is this enough events to capture all the trends in the data? So heres what I do to answer that question. I took my 1,000 events of data and I put about 800 into a training set and about 200 into a test set. Then I took these 800 and I split them again into 4 times 200. So then by recombining different numbers of these slices, I could come up with a training set that had 200