title: linearRegression() returns list within list (sklearn)
body: <p>I'm doing multivariate linear regression in Python (<a href="http://scikit-learn.org/stable/modules/linear_model.html#ordinary-least-squares" rel="noreferrer">sklearn</a>), but for some reason, the coefficients are not correctly returned as a list. Instead, a list IN A LIST is returned:</p>
  
  <pre><code>from sklearn import linear_model
  clf = linear_model.LinearRegression()
  # clf.fit ([[0, 0, 0], [1, 1, 1], [2, 2, 2]], [0, 1, 2])
  clf.fit([[394, 3878, 13, 4, 0, 0],[384, 10175, 14, 4, 0, 0]],[3,9])
  print 'coef array',clf.coef_
  print 'length', len(clf.coef_)
  print 'getting value 0:', clf.coef_[0]
  print 'getting value 1:', clf.coef_[1]
  </code></pre>
  
  <p>This returns the values in a list of a list [[]] instead of a list []. Any idea why this is happening? Output:</p>
  
  <pre><code>coef array [[  1.03428648e-03   9.54477167e-04   1.45135995e-07   0.00000000e+00
  0.00000000e+00   0.00000000e+00]]
  length 1
  getting value 0: [  1.03428648e-03   9.54477167e-04   1.45135995e-07   0.0000000
  0e+00 0.00000000e+00   0.00000000e+00]
  getting value 1:
  Traceback (most recent call last):
    File "regress.py", line 8, in &lt;module&gt;
      print 'getting value 1:', clf.coef_[1]
  IndexError: index out of bounds
  </code></pre>
  
  <p>But this works:</p>
  
  <pre><code>from sklearn import linear_model
  clf = linear_model.LinearRegression()
  clf.fit ([[0, 0, 0], [1, 1, 1], [2, 2, 2]], [0, 1, 2])
  # clf.fit([[394, 3878, 13, 4, 0, 0],[384, 10175, 14, 4, 0, 0]],[3,9])
  print 'coef array',clf.coef_
  print 'length', len(clf.coef_)
  print 'getting value 0:', clf.coef_[0]
  print 'getting value 1:', clf.coef_[1]
  </code></pre>
  
  <p>Output:</p>
  
  <pre><code>coef array [ 0.33333333  0.33333333  0.33333333]
  length 3
  getting value 0: 0.333333333333
  getting value 1: 0.333333333333
  </code></pre>