title: feature_importances_ showing up as NoneType in ExtraTreesClassifier :TypeError: 'NoneType' object is not iterable
body: <p>I am trying to select important features (or at least understand which features explain more variabilty) for a given dataset. Towards this I use both ExtraTreesClassifier and GradientBoostingRegressor - and then use :-</p>
  
  <pre><code>clf = ExtraTreesClassifier(n_estimators=10,max_features='auto',random_state=0) # stops after 10 estimation passes, right ?
  clf.fit(x_train, y_train)
  feature_importance=clf.feature_importances_  # does NOT work - returns NoneType for feature_importance
  </code></pre>
  
  <p>Post this <strong>I am really interested</strong> in plotting them(for visual representation) - or even preliminary, just <strong>looking at the relative order of importance and the corresponding indices</strong></p>
  
  <pre><code># Both of these do not work as the feature_importance is of NoneType
  feature_importance = 100.0 * (feature_importance / feature_importance.max())
  indices = numpy.argsort(feature_importance)[::-1]
  </code></pre>
  
  <p>What I found puzzling was - if I were to use GradientBoostingRegressor as below, I do get the feature_importance and the indices thereof. What am I doing wrong ?</p>
  
  <pre><code>#Works with GradientBoostingRegressor
  params = {'n_estimators': 100, 'max_depth': 3, 'learning_rate': 0.1, 'loss': 'lad'}
  clf = GradientBoostingRegressor(**params).fit(x_train, y_train)
  clf.fit(x_train, y_train)
  feature_importance=clf.feature_importances_
  </code></pre>
  
  <p><em>other info</em> : I have 12 independent vars(x_train) and one label var(y_train)) with multiple values (say 4,5,7) and type(x_train) is  and type(feature_importance) is  </p>
  
  <p><em>Acknowledgments</em> : Some elements are borrowed from this post  <a href="http://www.tonicebrian.com/2012/11/05/training-gradient-boosting-trees-with-python/" rel="nofollow">http://www.tonicebrian.com/2012/11/05/training-gradient-boosting-trees-with-python/</a></p>