There are different types of ensemble methods, including:
- Bagging (Bootstrap Aggregating): Bagging is an ensemble method that creates multiple bootstrapped samples from the training data. Each bootstrapped sample is used to train a base model, and the predictions of the base models are then averaged to make the final prediction.
- Boosting (Adaptive Boosting): Boosting is an ensemble method that trains base models sequentially. Each base model is trained on the same training data, but the data is re-weighted after each model is trained. The data points that are misclassified by the previous model are given a higher weight, so that the subsequent models focus on these data points.
- Random Forests: Random forests are an ensemble method that builds a set of decision trees. Each decision tree is trained on a different subset of the training data, and the final prediction is made by majority vote or averaging the predictions of the individual decision trees.
Ensemble methods are often more accurate than single models, as they can help to reduce the variance and bias of the model. They can also be used to improve the robustness of the model, as they can help to prevent the model from overfitting to the training data.