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- I first learnt about probability when I was in secondary school. As with all the other topics in Maths, it was just another bunch of formulas to memorize and regurgitate to apply to exam questions. Although I was curious if there was any use for it beyond calculating the odds for gambling, I didn't manage to find out any. As with many things in my life, things pop up at unexpected places and I stumbled on it again when as I started on machine learning and naive Bayesian classifiers.
+I first learnt about probability when I was in secondary school. As with all the other topics in Maths, it was just another bunch of formulas to memorize and regurgitate to apply to exam questions. Although I was curious if there was any use for it beyond calculating the odds for gambling, I didn't manage to find out any. As with many things in my life, things pop up at unexpected places and I stumbled on it again when as I started on machine learning and naive Bayesian classifiers.
A classifier is exactly that -- it's something that classifies other things. A classifier is a function that takes in a set of data and tells us which category or classification the data belongs to. A naive Bayesian classifier is a type of learning classifier, meaning that you can continually train it with more data and it will be be better at its job. The reason why it's called Bayesian is because it uses [Bayes Law](http://en.wikipedia.org/wiki/Bayes%27_theorem), a mathematical theorem that talks about conditional probabilities of events, to determine how to classify the data. The classifier is called 'naive' because it assumes each event (in this case the data) to be totally unrelated to each other. That's a very simplistic view but in practice it has been proven to be a surprisingly accurate. Also, because it's relatively simple to implement, it's quite popular. Amongst its more well-known usage include email spam filters.

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