Ensemble learning is a technique that combines the output of multiple classifiers also called weak learners to build a more robust prediction model. Ensemble methods work by combining a group of classifiers (or models) to get an enhanced prediction accuracy. The idea behind an “ensemble” is that the performance from the average of a group of classifiers will be better than each classifier on its own. So each classifier is called a “weak” learner.
Ensemble learners are usually high-performing algorithms for both classification and regression tasks ...
