What is stacking in machine learning and how does it work as an ensemble technique? How does stacking combine multiple base models to improve prediction accuracy? What is the role of a meta-model in stacking? How does stacking differ from bagging and boosting methods? What are the advantages and limitations of using stacking in real-world machine learning applications?