title: Sklearn - Cannot use encoded data in Random forest classifier
body: <p>I'm new to scikit-learn. I'm trying use preprocessing. OneHotEncoder to encode my training and test data. After encoding I tried to train Random forest classifier using that data. But I get the following error when fitting.
  (Here the error trace)</p>
  
  <pre><code>    99         model.fit(X_train, y_train)
      100         preds = model.predict_proba(X_cv)[:, 1]
      101 
  
  C:\Python27\lib\site-packages\sklearn\ensemble\forest.pyc in fit(self, X, y, sample_weight)
      288 
      289         # Precompute some data
  --&gt; 290         X, y = check_arrays(X, y, sparse_format="dense")
      291         if (getattr(X, "dtype", None) != DTYPE or
      292                 X.ndim != 2 or
  
  C:\Python27\lib\site-packages\sklearn\utils\validation.pyc in check_arrays(*arrays, **options)
      200                     array = array.tocsc()
      201                 elif sparse_format == 'dense':
  --&gt; 202                     raise TypeError('A sparse matrix was passed, but dense '
      203                                     'data is required. Use X.toarray() to '
      204                                     'convert to a dense numpy array.')
  
  TypeError: A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array.
  </code></pre>
  
  <p>I tried to convert the sparse matrix into dense using X.toarray() and X.todense()
  But when I do that, I get the following error trace. </p>
  
  <pre><code> 99         model.fit(X_train.toarray(), y_train)
      100         preds = model.predict_proba(X_cv)[:, 1]
      101 
  
  C:\Python27\lib\site-packages\scipy\sparse\compressed.pyc in toarray(self)
      548 
      549     def toarray(self):
  --&gt; 550         return self.tocoo(copy=False).toarray()
      551 
      552     ##############################################################
  
  C:\Python27\lib\site-packages\scipy\sparse\coo.pyc in toarray(self)
      236 
      237     def toarray(self):
  --&gt; 238         B = np.zeros(self.shape, dtype=self.dtype)
      239         M,N = self.shape
      240         coo_todense(M, N, self.nnz, self.row, self.col, self.data, B.ravel())
  
  ValueError: array is too big.
  </code></pre>
  
  <p>Can anyone help me to fix this.</p>
  
  <p>Thank you</p>