Ensemble Learning in machine learning integrates multiple models called weak learners to create a single effective model for prediction. This technique is used to enhance accuracy, minimizing variance and removing overfitting. Here we will learn different ensemble techniques and their algorithms. Main types of ensemble models 1. Bagging - Bootstrap Aggregating.** Bagging is a technique that involves creating multiple versions of a model and combining their outputs to improve overall performance. In bagging several base models are trained on different subsets of the training data, then aggregate their predictions to make the final decision. The subsets of the data are created using bootstrapping, a statistical technique where samples are drawn with replacement, meaning some data points can appear more than once in a subset. The final prediction from the ensemble is typically made by either: Averaging the predictions (for regression problems), or Majority voting (for classification problems).…