title: KNN with RFECV returns: "The classifier does not expose "coef_" or "feature_importances_" attributes"
body: <p>I am trying to apply RFECV on KNeighborsClassifier to eliminate insignificant features. In order to make the issue repeatable, here is an example with iris data:</p>
  
  <pre><code>from sklearn.datasets import load_iris
  from sklearn.feature_selection import RFECV
  from sklearn.neighbors import KNeighborsClassifier
  iris = load_iris()
  y = iris.target
  X = iris.data
  estimator = KNeighborsClassifier()
  selector = RFECV(estimator, step=1, cv=5)
  selector = selector.fit(X, y)
  </code></pre>
  
  <p>which results in the following error massage:</p>
  
  <pre><code>---------------------------------------------------------------------------
  RuntimeError                              Traceback (most recent call last)
  &lt;ipython-input-27-19f0f2f0f0e7&gt; in &lt;module&gt;()
        7 estimator = KNeighborsClassifier()
        8 selector = RFECV(estimator, step=1, cv=5)
  ----&gt; 9 selector.fit(X, y)
  
  C:...\Anaconda3\lib\site-packages\sklearn\feature_selection\rfe.py in fit(self, X, y)
      422                       verbose=self.verbose - 1)
      423 
  --&gt; 424             rfe._fit(X_train, y_train, lambda estimator, features:
      425                      _score(estimator, X_test[:, features], y_test, scorer))
      426             scores.append(np.array(rfe.scores_[::-1]).reshape(1, -1))
  
  C:...\Anaconda3\lib\site-packages\sklearn\feature_selection\rfe.py in _fit(self, X, y, step_score)
      180                 coefs = estimator.feature_importances_
      181             else:
  --&gt; 182                 raise RuntimeError('The classifier does not expose '
      183                                    '"coef_" or "feature_importances_" '
      184                                    'attributes')
  
  RuntimeError: The classifier does not expose "coef_" or "feature_importances_" attributes
  </code></pre>
  
  <p>If I change the classifier to a SVC as:</p>
  
  <pre><code>from sklearn.datasets import load_iris
  from sklearn.feature_selection import RFECV
  from sklearn.svm import SVC
  iris = load_iris()
  y = iris.target
  X = iris.data
  estimator = SVC(kernel="linear")
  selector = RFECV(estimator, step=1, cv=5)
  selector = selector.fit(X, y)
  </code></pre>
  
  <p>it would work fine. Any suggestions on how to address the issue? </p>
  
  <p><strong>NOTE:</strong> I updated Anaconda yesterday which also updated the sklearn.</p>