input: from sklearn.svm import SVC
  
  def evaluate_model_accuracy(X_train, y_train, X_test, y_test) -> float:
      """
      Evaluate the accuracy of a machine-learning model.
      
      >>> from sklearn.svm import SVC
      >>> X_train, y_train = [[0], [1]], [0, 1]
      >>> X_test, y_test = [[2]], [1]
      >>> evaluate_model_accuracy(X_train, y_train, X_test, y_test)
      1.0
      """
      clf = SVC(kernel='poly', degree=2, gamma=0.05)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy
output:     from sklearn.metrics import accuracy_score