sklearn's TfidfVectorizer word frequency?
<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>