Ensemble
A machine learning technique that combines the predictions from multiple distinct models to produce a single, more accurate, and highly robust final output.
Think of It Like This
Like a jury where multiple people with different perspectives discuss a case to reach a verdict that is fairer than any single person's decision.
Ensemble methods operate on the principle that a group of weak learners can form a strong learner. By aggregating outputs through voting or averaging, these techniques effectively reduce variance and mitigate overfitting. Common strategies include bagging, boosting, and stacking, all of which generally outperform individual base models.