old_contents: """
  Train the decision tree that we've determined gives the best performance on
  our training data.  See the decision_tree ipython notebook for more details.
  """
  
  import cPickle as pickle
  from sklearn import tree
  from lignet_utils import gen_train_test
  
  x_train, x_test, y_train, y_test, x_scaler, y_scaler = gen_train_test()
  
  dtr_full = tree.DecisionTreeRegressor(max_depth=26, min_samples_leaf=2)
  dtr_full = dtr_full.fit(x_train, y_train)
  
  # pickle the trained regressor
  with open('trained_networks/decision_tree.pkl', 'wb') as pkl:
      pickle.dump([dtr_full], pkl)
new_contents: """
  Train the decision tree that we've determined gives the best performance on
  our training data.  See the analyze_decision_tree ipython notebook for more
  details.
  """
  
  import cPickle as pickle
  from sklearn import tree
  from lignet_utils import gen_train_test
  
  x_train, x_test, y_train, y_test, x_scaler, y_scaler = gen_train_test()
  
  dtr_full = tree.DecisionTreeRegressor(max_depth=26, min_samples_leaf=2)
  dtr_full = dtr_full.fit(x_train, y_train)
  
  # pickle the trained regressor
  with open('trained_networks/decision_tree.pkl', 'wb') as pkl:
      pickle.dump([dtr_full], pkl)
subject: update docstring