title: Extract an MLPRegressor attributes (n_iter_ ) for the best model in a pipeline with GridsearchCV?
body: <p>I made a <code>GridsearchCV</code> with a pipeline and I want to extract one attribute (<code>n_iter_</code>) of a component of the pipeline (MLPRegressor) for the best model.</p>
  
  <p>I'm using Python 3.0.</p>
  
  <p><strong>Creation of the pipeline</strong></p>
  
  <pre><code>pipeline_steps = [('scaler', StandardScaler()), ('MLPR', MLPRegressor(solver='lbfgs', early_stopping=True, validation_fraction=0.1, max_iter=10000))]
  
  MLPR_parameters = {'MLPR__hidden_layer_sizes':[(50,), (100,), (50,50)], 'MLPR__alpha':[0.001, 10, 1000]}
  
  MLPR_pipeline = Pipeline(pipeline_steps)
  
  gridCV_MLPR = GridSearchCV(MLPR_pipeline, MLPR_parameters, cv=kfold)
  gridCV_MLPR.fit(X_train, y_train)
  </code></pre>
  
  <p>When I want to extract the best model with <code>gridCV_GBR.best_params_</code>, I only have the result for the GridsearchCV :</p>
  
  <pre><code>{'MLPR__alpha': 0.001, 'MLPR__hidden_layer_sizes': (50,)}
  </code></pre>
  
  <p>But I want to have the number of iteration of MLPRegressor used by the best model of <code>gridCV_MLPR</code>.</p>
  
  <p>How is it possible to use the <code>n_iter_</code> attribute designed for <code>MLPRegressor()</code> through the pipeline with GridsearhCV ?</p>