title: modeling feature set with text documents
body: <p>Example:</p>
  
  <p>I have m sets of ~1000 text documents, ~10 are predictive of a binary result,  roughly 990 aren't.</p>
  
  <p>I want to train a classifier to take a set of documents and predict the binary result.</p>
  
  <p>Assume for discussion that the documents each map the text to 100 features.</p>
  
  <p>How is this modeled in terms of training examples and features? Do I merge all the text together and map it to a fixed set of features? Do I have 100 features per document * ~1000 documents (100,000 features) and one training example per set of documents? Do I classify each document separately and analyze the resulting set of confidences as they relate to the final binary prediction?</p>