title: Using scikit-learn to train on multidimensional data
body: <p>It's a very basic concept: I have more than one dependency for training. My data is all text and I have three separate fields. Every example I have been able to find has text data set up like this:</p>
  
  <pre><code>data = ['text1','text2',...]
  </code></pre>
  
  <p>where mine looks like:</p>
  
  <pre><code>data = [['text1','text2','text3'],[...],...]
  </code></pre>
  
  <p>but when I try and fit to the data I get the following traceback:</p>
  
  <pre><code>ValueError                                Traceback (most recent call last)
  &lt;ipython-input-25-e3356a0f62f8&gt; in &lt;module&gt;()
  ----&gt; 1 classifier.fit(X,y)
  
  /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/svm/base.pyc in fit(self, X, y, sample_weight)
      140                              "by not using the ``sparse`` parameter")
      141 
  --&gt; 142         X = atleast2d_or_csr(X, dtype=np.float64, order='C')
      143 
      144         if self.impl in ['c_svc', 'nu_svc']:
  
  /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/validation.pyc in atleast2d_or_csr(X, dtype, order, copy)
      114     """
      115     return _atleast2d_or_sparse(X, dtype, order, copy, sparse.csr_matrix,
  --&gt; 116                                 "tocsr")
      117 
      118 
  
  /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/validation.pyc in _atleast2d_or_sparse(X, dtype, order, copy, sparse_class, convmethod)
       94         _assert_all_finite(X.data)
       95     else:
  ---&gt; 96         X = array2d(X, dtype=dtype, order=order, copy=copy)
       97         _assert_all_finite(X)
       98     return X
  
  /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/validation.pyc in array2d(X, dtype, order, copy)
       78         raise TypeError('A sparse matrix was passed, but dense data '
       79                         'is required. Use X.toarray() to convert to dense.')
  ---&gt; 80     X_2d = np.asarray(np.atleast_2d(X), dtype=dtype, order=order)
       81     _assert_all_finite(X_2d)
       82     if X is X_2d and copy:
  
  /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/numeric.pyc in asarray(a, dtype, order)
      318 
      319     """
  --&gt; 320     return array(a, dtype, copy=False, order=order)
      321 
      322 def asanyarray(a, dtype=None, order=None):
  
  ValueError: setting an array element with a sequence.
  </code></pre>
  
  <p>is there a specific way I have to approach this? Thank you!</p>
  
  <p>NOTES:</p>
  
  <p>All of the text data I am using is vectorized by a <code>HashingVectorizer</code></p>
  
  <p><code>clf.fit(X,y)</code> where <code>X</code> is a list of lists that contain 3 vectorized texts, and <code>y</code> is a list of the respective categories that the element of <code>X</code> belongs to</p>