input: 
  def evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test):
      """ Evaluate the accuracy of a given machine-learning model by training it on the provided 
      training data and then predicting on the test data. Return the calculated accuracy as a float.
      >>> from sklearn.svm import SVC
      >>> clf = SVC(kernel='poly', degree=2, gamma=0.05)
      >>> X_train = [[0], [1], [2], [3]]
      >>> y_train = [0, 1, 2, 3]
      >>> X_test = [[1], [3]]
      >>> y_test = [1, 3]
      >>> evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test)
      1.0
      """
      
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      return accuracy_score(y_test, y_pred)
  
output:     from sklearn.metrics import accuracy_score