Part 2/11:
Ensemble learning can be likened to a team of experts working collaboratively to tackle a complex problem. Instead of relying on a single model, ensemble learning blends multiple models—often referred to as weak learners or base models—to harness their collective intelligence. This collaborative method reduces errors and enhances predictions, echoing the wisdom of crowds. For example, when deciding where to dine, consulting five friends can lead to a more satisfying decision than solely depending on one person's recommendation.