new_contents: #!/usr/bin/env python
  
  import numpy as np
  from sklearn import linear_model
  from sklearn import tree
  from sklearn import svm
  from sklearn import metrics
  
  # Read train and test data
  with open("../data/cancer.csv", "r") as f:
      train_dataset = np.loadtxt(f, delimiter=",")
      labels = train_dataset[:, 9]
      features = train_dataset[:, 0:9]
  
  with open("../data/cancer_test.csv", "r") as f:
      test_dataset = np.loadtxt(f, delimiter=",")
      test_labels = test_dataset[:, 9]
      test_features = test_dataset[:, 0:9]
  
  # Define the model
  classifier = tree.DecisionTreeClassifier()
  #classifier = svm.SVC(C=1, kernel='linear')
  
  # Train the model
  model = classifier.fit(features, labels)
  predict_labels = model.predict(test_features)
  #fpr, tpr, thresholds = metrics.roc_curve(test_labels, predict_labels)
  auc = metrics.roc_auc_score(test_labels, predict_labels)
  accuracy = metrics.accuracy_score(test_labels, predict_labels)
  
  # Print the metrics
  print("Accuracy: {}, acu: {}".format(accuracy, auc))
  
  
subject: Add sklearn example