Ensemble learning is a machine learning technique that involves combining the predictions of multiple individual models to create a more accurate and robust prediction. It’s a way to improve the performance of a model by leveraging the strengths of different algorithms. The concept behind ensemble learning is that by aggregating the predictions of multiple models, the errors made by individual models can be mitigated, leading to an overall better prediction.

There are several popular methods of ensemble learning, including:

1. Bagging (Bootstrap Aggregating): This involves training multiple instances of the same model on different subsets of the training data. Each model is trained independently, and their predictions are then averaged or combined in some way.
2. Boosting: In boosting, multiple weak models are trained sequentially. Each new model focuses on the examples that the previous models misclassified, therefore giving more importance to the misclassified instances.
3. Random Forest: A specific type of ensemble method that uses bagging and decision trees. It builds a collection of decision trees and combines their predictions for a more accurate outcome.
4. Stacking: Stacking combines multiple models of different types. A meta-model is trained to take the predictions of the base models as inputs and generate the final prediction.
5. Voting: In this approach, each model gets one vote, and the class that receives the most votes is the final prediction. This can be applied to classification problems.