Ensemble method Algorithms  Concepts of Ensemble methods E nsemble methods - bagging, boosting, and stacking Ensemble methods are a type of machine learning technique that combines multiple individual models to improve the overall performance of the system. There are several ensemble methods, but three of the most commonly used ones are: Bagging (Bootstrap Aggregating) : Bagging is a technique that uses multiple independent models in parallel to make a prediction. Each model is trained on a subset of the training data, which is randomly sampled with replacement. The final prediction is made by averaging the predictions of all the individual models. Boosting : Boosting is a technique that uses multiple models sequentially. Each model is trained on the entire training set, but the weights of the training examples are adjusted based on the performance of the previous model. The final prediction is made by combining the predictions of all the individual models. Stacking : Stacking i