title: Sci-kit Learn Linear Regression with multiple feature
body: <p>I was working with Sci-kit Learn for linear regresion problem. In Sci-kit Learn website there is an example for one feature </p>
  
  <pre><code>diabetes = datasets.load_diabetes()
  
  
  # Use only one feature
  diabetes_X = diabetes.data[:, np.newaxis, 2]
  </code></pre>
  
  <p>I tried to change code little bit. </p>
  
  <pre><code>diabetes_X = diabetes.data[:, np.newaxis, [0,1,2]]
  </code></pre>
  
  <p>When i printed out this variable i've got something like this:</p>
  
  <p><strong>[[ 0.03807591  0.05068012  0.06169621]] etc.</strong></p>
  
  <p>So i can say that i've got 3 features right now. If i try to fit this training data to my model i've got this error:</p>
  
  <pre><code>regr.fit(diabetes_X_train, diabetes_y_train)
    File "/Library/Python/2.7/site-packages/sklearn/linear_model/base.py", line 427, in fit
      y_numeric=True, multi_output=True)
    File "/Library/Python/2.7/site-packages/sklearn/utils/validation.py", line 510, in check_X_y
      ensure_min_features, warn_on_dtype, estimator)
    File "/Library/Python/2.7/site-packages/sklearn/utils/validation.py", line 396, in check_array
      % (array.ndim, estimator_name))
  ValueError: Found array with dim 3. Estimator expected &lt;= 2.
  </code></pre>
  
  <p>I've found only one feature examples on google so i don't how to train my model with multiple feature. Should i create a multiple variable and fit each feature to each variable? I hope i told my problem clearly.</p>