Bagging, Boosting & Stacking Technique Introduction: Bagging and boosting are two ensemble learning techniques commonly used in machine learning. Both approaches aim to improve the predictive performance of individual models by combining multiple models together. However, they differ in how they construct and combine the models. (1) Bagging Technique:(Bootstrap Aggregating): Bagging involves creating multiple copies of the original training dataset through a technique called bootstrapping. Bootstrapping randomly samples the training data with replacement, resulting in different subsets of data for each model. Each model in the ensemble is trained independently on one of the bootstrapped datasets. Bagging typically uses majority

Read More