Ensemble Learning
In plain terms
Combining multiple models to improve predictions.
Going deeper
Ensemble learning aggregates several models so their combined output is more accurate and robust than any single one. Diversity cancels individual errors.
Under the hood
Bagging reduces variance, boosting reduces bias, stacking blends models; powers many winning solutions.
Examples
Random forests, gradient boosting, voting, stacking, Kaggle solutions.
Think of it like...
Like a panel of judges whose averaged score beats any single judge's.