title: Using scikit learn's GaussianNB with nltk doesn't work
body: <p>I am trying to use nltk's wrapper for scikit-learn's classifiers. I use this code to train the classifier:</p>
  
  <pre><code>classifier = SklearnClassifier(GaussianNB())
  classifier.train(self.training_set)
  </code></pre>
  
  <p>Where <code>training_set</code> looks like </p>
  
  <pre><code>[({'name':'Alpha Hotel', 'clicks':765, 'zip_code':75025},'no bookings')]
  </code></pre>
  
  <p>The error I am getting is </p>
  
  <blockquote>
    <p>TypeError: A sparse matrix was passed, but dense data is required. Use
    X.toarray() to convert to a dense numpy array.</p>
  </blockquote>
  
  <p>I don't know how to convert to a dense array, especially since nltk's documentation for the <code>train</code> method requires <code>A list of (featureset, label) where each featureset is a dict mapping strings to either numbers, booleans or strings.</code></p>