new_contents: from abc import abstractmethod
  from sklearn.ensemble import RandomForestClassifier
  import numpy as np
  
  
  class ForestPredictor(object):
      """"
      A simple application of RandomForestClassifier
  
      @author: Shaun
      """
  
      def __init__(self):
          self.clf = RandomForestClassifier()
  
      @abstractmethod
      def fit(self, X, y):
          """
          Method to fit the model.
  
          Parameters:
          X - 2d numpy array of training data
          y - 1d numpy array of training labels
          """
          self.clf = self.clf.fit(X, y)
  
      @abstractmethod
      def predict(self, X):
          """
          Method to apply the model data
  
          Parameters:
          X - 2d numpy array of test data
          """
          return self.clf.predict_proba(X)[:, 1]
  
  if __name__ == '__main__':
      N = 1000
      D = 2
      X = np.random.rand(N, D)
      y = np.random.randint(0, 2, N)
  
      predictor = ForestPredictor()
      predictor.fit(X, y)
  
      x = np.random.rand(1, D)
      pred = predictor.predict(x)
  
      print pred
  
  
  
  
  
subject: Add forest Predictor.