In this edition, we’re exploring ensemble methods, focusing on stacking and voting, and how they can help you get more performance out of your models. By the time you’re completed, you’ll have a clear understanding of what voting classifiers are, when to use them, and why they often outperform a single model on its own. And because theory only takes you so far, we’ll also explore how these concepts fit into Microsoft Fabric, so you can go from experimenting in a notebook to applying them in real-world projects.