title: Incremental training of random forest model using python sklearn
body: <p>I am using the below code to save a random forest model. I am using cPickle to save the trained model. As I see new data, can I train the model incrementally. 
  Currently, the train set has about 2 years data. Is there a way to train on another 2 years and (kind of) append it to the existing saved model. </p>
  
  <pre><code>rf =  RandomForestRegressor(n_estimators=100)
  print ("Trying to fit the Random Forest model --&gt; ")
  if os.path.exists('rf.pkl'):
      print ("Trained model already pickled -- &gt;")
      with open('rf.pkl', 'rb') as f:
          rf = cPickle.load(f)
  else:
      df_x_train = x_train[col_feature]
      rf.fit(df_x_train,y_train)
      print ("Training for the model done ")
      with open('rf.pkl', 'wb') as f:
          cPickle.dump(rf, f)
  df_x_test = x_test[col_feature]
  pred = rf.predict(df_x_test)
  </code></pre>
  
  <p>EDIT 1: I don't have the compute capacity to train the model on 4 years of data all at once.</p>