input: Write a function to evaluate the accuracy of a machine-learning model using the accuracy_score function from sklearn.metrics. The function should fit the model on the training data and then predict on the test data. Finally, it should return the accuracy of the predictions.
  assert evaluate_accuracy(SVC(kernel='poly', degree=2, gamma=0.05), X_train, y_train, X_test, y_test)==0.9
  assert evaluate_accuracy(SVC(kernel='rbf', gamma='scale'), X_train, y_train, X_test, y_test)==0.85
  assert evaluate_accuracy(SVC(kernel='linear'), X_train, y_train, X_test, y_test)==0.92
  assert evaluate_accuracy(SVC(kernel='sigmoid'), X_train, y_train, X_test, y_test)==0.88
output: def evaluate_accuracy(model, X_train, y_train, X_test, y_test):
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)