title: spam classification - machine learning
body: <p>I have to do spam detection application using a few classifiers(e.g. Naive Bayes, SVM and another one yet) and compare them efficiency but unfortunately I don't know what should I do exactly.</p>
  
  <p>Is this correct:
  Firstly I should have corpus spam such as trec2005, spamassasin or enron-spam.
  Then, I do text pre-processing like stemming, stop words removal, tokenize, etc.</p>
  
  <p>After that I can measure weight my features/terms in spam emails using tf-idf . 
  Next I remove these features with very low and very high frequencies.
  And I can classify my emails then. Right?</p>
  
  <p>After that I can measure my correct classifications by true-positive, false-positive, etc..</p>
  
  <p>If 10fold cross validation is required for something ?
  How should I use it?</p>
  
  <p>Could you tell me if these steps for email classifications are OK?
  If not, please explain what are the correct steps for spam classification.</p>