Ensemble methods are a type of machine learning algorithm that combines the predictions of multiple individual models to make more accurate predictions than any of the individual models alone. This is achieved by training multiple models on the same dataset and then combining their predictions through some mathematical function, such as a weighted average or majority vote.

Two popular examples of ensemble methods are bagging and boosting.

Bagging, also known as bootstrapped aggregation, is a simple ensemble method that involves training multiple models on different subsets of the training data. Each model is trained on a different random subset of the data, and the final prediction is made by combining the predictions of all the individual models. This is often done using a majority vote, where the predicted class is the one that is predicted by the most individual models.

Boosting is a more sophisticated ensemble method that involves training multiple models in a sequential manner. In boosting, each model is trained to correct the mistakes of the previous model, with the goal of improving the overall performance of the ensemble. One popular boosting algorithm is AdaBoost, which works by assigning higher weights to training examples that were misclassified by previous models, so that the next model in the sequence focuses more on these difficult cases.

Ensemble methods are effective because they can reduce overfitting, improve generalization, and increase model performance. By training multiple models on different subsets of the data, bagging can reduce overfitting by decreasing the variance of the individual models. Boosting can improve generalization by giving more weight to difficult cases, which can help the ensemble to make more accurate predictions on unseen data. Overall, ensemble methods can often achieve higher performance than any of the individual models alone.