content: #!/usr/bin/python
  
  
  """
      starter code for the evaluation mini-project
      start by copying your trained/tested POI identifier from
      that you built in the validation mini-project
  
      the second step toward building your POI identifier!
  
      start by loading/formatting the data
  
  """
  
  import pickle
  import sys
  sys.path.append("C:/Vindico/Projects/Code/Python/Python/Course/Udacity/Intro to Machine Learning/ud120-projects-master/tools/")
  from feature_format import featureFormat, targetFeatureSplit
  from sklearn.tree import DecisionTreeClassifier
  from sklearn import cross_validation
  import numpy as np
  
  data_dict = pickle.load(open("C:/Vindico/Projects/Code/Python/Python/Course/Udacity/Intro to Machine Learning/ud120-projects-master/final_project/final_project_dataset.pkl", "r") )
  
  ### add more features to features_list!
  features_list = ["poi", "salary"]
  
  data = featureFormat(data_dict, features_list)
  labels, features = targetFeatureSplit(data)
  
  
  
  ### your code goes here 
  features_train,features_test,labels_train,labels_test = cross_validation.train_test_split(features,labels,test_size=0.3,
                                                                                            random_state=42)
  clf = DecisionTreeClassifier()
  clf.fit(features_train,labels_train)
  clf.score(features_test,labels_test)
  
  # How many POIs are in the test set for your POI identifier?
  pred = clf.predict(features_test)
  sum(pred)
  print len([e for e in labels_test if e == 1.0])
  
  # How many people total are in your test set?
  len(pred)
  
  # If your identifier predicted 0. (not POI) for everyone in the test set, what would its accuracy be?
  1.0 - 5.0/29
  
  # Precision and recall can help illuminate your performance better. 
  # Use the precision_score and recall_score available in sklearn.metrics to compute those quantities.
  
  # What’s the precision?
  from sklearn.metrics import *
  
  precision_score(labels_test, pred)
  
  # What’s the recall? 
  recall_score(labels_test, pred)
  
  # Here are some made-up predictions and true labels for a hypothetical test set; 
  # fill in the following boxes to practice identifying true positives, false positives, true negatives, and false negatives. 
  # Let’s use the convention that “1” signifies a positive result, and “0” a negative. 
  predictions = [0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1] 
  true_labels = [0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0]
  
  # What's the precision of this classifier?
  precision_score(true_labels, predictions)
  
  # What's the recall of this classifier?
  recall_score(true_labels, predictions)
  
  