old_contents: import sklearn.cross_validation
  
  class Backend(object):
      def __init__(self):
          self.dataset_id = 0
          self.dataset = None
          self.model = None
  
      def load(self, dataset):
          self.model = None
          self.dataset_id += 1
          self.dataset = dataset
          self._data = dataset['data']
          self._target = dataset['target']
          return True
  
      def loaded(self):
          return (self.dataset is not None)
  
      def fit(self):
          if not self.loaded():
              return False
          model = self.classifier()
          model.fit(self._data, self._target)
          self.model = model
          return True
  
      def trained(self):
          return (self.model is not None)
  
      def validate(self, test_size = 0.2, random_state = 0):
          if not self.loaded():
              return
          model = self.classifier()
          X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(
              self._data, self._target, test_size = test_size, random_state = random_state
          )
          model.fit(X_train, y_train)
          return [model.score(X_test, y_test)]
  
      def predict(self, value):
          return self.model.predict(value)
  
new_contents: import sklearn.cross_validation
  
  class Backend(object):
      def __init__(self):
          self.dataset_id = 0
          self.random_id = 0
          self.dataset = None
          self.model = None
  
      def load(self, dataset):
          self.model = None
          self.dataset_id += 1
          self.dataset = dataset
          self._data = dataset['data']
          self._target = dataset['target']
          return True
  
      def loaded(self):
          return (self.dataset is not None)
  
      def fit(self):
          if not self.loaded():
              return False
          model = self.classifier()
          model.fit(self._data, self._target)
          self.model = model
          return True
  
      def trained(self):
          return (self.model is not None)
  
      def validate(self, test_size = 0.2):
          if not self.loaded():
              return
          self.random_id += 1
          model = self.classifier()
          X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(
              self._data, self._target, test_size = test_size, random_state = self.random_id
          )
          model.fit(X_train, y_train)
          return [model.score(X_test, y_test)]
  
      def predict(self, value):
          return self.model.predict(value)
  
subject: Validate with sequential random state