input: def evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test):
      """
      Evaluate the accuracy of a machine-learning model using provided training and testing datasets.
      
      >>> from sklearn.svm import SVC
      >>> clf = SVC(kernel='poly', degree=2, gamma=0.05)
      >>> X_train, y_train, X_test, y_test = [[1, 2], [3, 4]], [0, 1], [[5, 6]], [0]
      >>> evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test)
      Accuracy: 1.0
      """
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return f"Accuracy: {accuracy}"
output:     from sklearn.metrics import accuracy_score