title: Naive bayes classification for NO text-classification
body: <p>Modelling the feature space for a text document is quite easy.<br>For example, I can take every word from a text (training data) as a feature.<br>If a particular word (e.g. "dog") encounters multiple times in (classified) training examples (e.g. classified as spam), then I can take this word to classify new data.</p>
  
  <p>How do I model my features, if they arent only words?<br>In my specific case, I have features like name, age and family size.<br>I don't think it is the right way to make an entry for every possible age in my feature vector.<br>If I assume that humans die by no later than 100, I would have 100 digits only for my age feature.<br>
  So I thought about data binning: Partitionate the feature "age" in maybe 1-20 yo, 21-40 yo, 41-60,...<br>
  To model a person with the age of 30 I would only need 5 digits now (01000).<br></p>
  
  <p>Is there a better way to model features like these?</p>