Sci-kit Learn Linear Regression with multiple feature
<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>