title: Using Sci-Kit learn to classify text with a large corpus
body: <p>I have about 1600 articles in my database, with each article already pre-labeled with one of the following categories:</p>
  
  <pre><code>Technology
  Science
  Business
  World
  Health
  Entertainment
  Sports
  </code></pre>
  
  <p>I am trying to use sci-kit learn to build a classifier that would categorize new articles. (I guess i'll split my training data in half, for training and testing?)</p>
  
  <p>I am looking to use tf-idf, as I don't have a list of stop-words (I can use NLTK to extract only adjectives and nouns, though, but i'd rather give scikit-learn the full article).</p>
  
  <p>I've read all of the documentation on scikit-learn, but their examples involve word-occurence and N-grams (which are fine), but they never specify how to tie a piece of data to a label.</p>
  
  <p>I've tried looking at their sample code, but it's too confusing to follow.</p>
  
  <p>Could someone help me with this, or point me in the right direction?</p>
  
  <p>Thanks.</p>