title: sklearn's TfidfVectorizer word frequency?
body: <p>I have a question about sklearn's TfidfVectorizer when it's doing the frequency of the word in each documents.</p>
  
  <p>the sample code I saw is:</p>
  
  <pre><code>&gt;&gt;&gt; from sklearn.feature_extraction.text import TfidfVectorizer
  
  &gt;&gt;&gt; corpus = [
  
  &gt;&gt;&gt;     'The dog ate a sandwich and I ate a sandwich',
  &gt;&gt;&gt;     'The wizard transfigured a sandwich'
  &gt;&gt;&gt; ]
  
  &gt;&gt;&gt; vectorizer = TfidfVectorizer(stop_words='english')
  
  &gt;&gt;&gt; print vectorizer.fit_transform(corpus).todense()
  
  [[ 0.75458397  0.37729199  0.53689271  0.          0.        ]
  [ 0.          0.          0.44943642  0.6316672   0.6316672 ]]
  </code></pre>
  
  <p>my question is: how do I interpret the numbers in the matrix? I understand the 0 means that the word i.e. wizard appears 0 times in the first document therefore it's 0, but how do I interpret the number 0.75458397? Is it the frequency that the word "ate" appeared in the first document? Or the frequency of the word "ate" that occurs in the entire corpus? </p>