The article introduces a method called stacked generalization to improve the accuracy of machine learning models. It works by combining the predictions of multiple models to make more accurate guesses. The researchers showed that stacked generalization outperformed individual models in tasks like translating text to phonemes and fitting surfaces. They suggest that using stacked generalization can help minimize errors in real-world problems.