Bagging systems, short for bootstrap aggregating, are ensemble machine learning techniques that improve the accuracy of models. They work by creating multiple subsets of the training data through random sampling with replacement. Each subset is used to train a separate model, and the final prediction is made by averaging the outputs of these models for regression tasks or by majority voting for classification tasks. This method helps to reduce overfitting, as it combines the strengths of various models while minimizing their individual weaknesses. Common algorithms that utilize bagging include Random Forests and Bagged Decision Trees, which are widely used in various applications for their robustness and effectiveness.