title: How to use GridSearchCV output for a scikit prediction?
body: <p>In the following code:</p>
  
  <pre><code># Load dataset
  iris = datasets.load_iris()
  X, y = iris.data, iris.target
  
  rf_feature_imp = RandomForestClassifier(100)
  feat_selection = SelectFromModel(rf_feature_imp, threshold=0.5)
  
  clf = RandomForestClassifier(5000)
  
  model = Pipeline([
            ('fs', feat_selection), 
            ('clf', clf), 
          ])
  
   params = {
      'fs__threshold': [0.5, 0.3, 0.7],
      'fs__estimator__max_features': ['auto', 'sqrt', 'log2'],
      'clf__max_features': ['auto', 'sqrt', 'log2'],
   }
  
   gs = GridSearchCV(model, params, ...)
   gs.fit(X,y)
  </code></pre>
  
  <p><strong>What should be used for a prediction?</strong></p>
  
  <ul>
  <li><code>gs</code>?</li>
  <li><code>gs.best_estimator_</code>?
  or</li>
  <li><code>gs.best_estimator_.named_steps['clf']</code>?</li>
  </ul>
  
  <p><strong>What is the difference between these 3?</strong></p>