new_contents: import numpy as np
  from sklearn.grid_search import GridSearchCV
  from sklearn.metrics import classification_report
  from sklearn.tree import DecisionTreeClassifier
  from sklearn import cross_validation
  
  import data_util as util
  
  bc_data, bc_target = util.load_breast_cancer()
  bc_dc = DecisionTreeClassifier()
  param_grid = {'criterion': ["gini", "entropy"]}
  clf = GridSearchCV(bc_dc, param_grid, n_jobs=1, cv=10, verbose=2)
  clf.fit(bc_data, bc_target)
  print "Best estimator: ", clf.best_estimator_
  print "Best parameters set found: ", clf.best_params_
  print classification_report(clf.grid_scores_)
  
subject: Use decision trees