Ensemble methods are a collection of classification algorithms that work together to improve accuracy and robustness. They combine the predictions of multiple base classifiers to make a final prediction, often outperforming individual classifiers. Ensemble methods are widely used and have proven to be highly effective in various domains and applications.

One popular ensemble method is bagging, which involves training multiple base classifiers on different subsets of the training data using bootstrapping. Each base classifier then makes a prediction, and the final prediction is determined by aggregating the predictions from all classifiers, such as by majority voting or averaging.

Another well-known ensemble method is boosting, which aims to sequentially build a strong classifier by focusing on training instances that were misclassified by previous base classifiers. Each base classifier is trained iteratively, and their predictions are combined using weighted voting or averaging.

Random forest is another powerful ensemble method that combines the concepts of bagging and decision trees. It creates an ensemble of decision trees, where each tree is trained on a random subset of the features and the final prediction is obtained through majority voting of the individual tree predictions.