Bagging, short for Bootstrap Aggregating, is an ensemble learning technique designed to improve the stability and accuracy of machine learning models. It works by generating multiple subsets of a training dataset through a process called bootstrapping, where samples are drawn with replacement. Each subset is used to train a separate model, often referred to as a “weak learner.” The final prediction is made by aggregating the predictions from all individual models, typically through averaging for regression tasks or majority voting for classification tasks. This approach effectively reduces variance and helps prevent overfitting, leading to more robust model performance.