input: from sklearn.metrics import accuracy_score
  
  def evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test) -> float:
      """ Evaluate the accuracy of a given machine-learning model.
      >>> clf = SVC(kernel='poly', degree=2, gamma=0.05)
      >>> clf.fit([[1,2],[2,3],[3,4]], [0,1,0])
      >>> evaluate_model_accuracy(clf, [[1,2],[2,3],[3,4]], [0,1,0], [[4,5],[5,6]], [1,0])
      0.5
      """
output:     clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      return accuracy_score(y_test, y_pred)