Bagging
Bagging, short for Bootstrap Aggregating, is an ensemble machine learning technique that improves the stability and accuracy of algorithms by combining multiple models trained on random subsets of the data. It works by generating multiple versions of a predictor (e.g., decision trees) using bootstrapped samples from the training dataset and then aggregating their predictions, typically through averaging for regression or majority voting for classification. This approach reduces variance and helps prevent overfitting, making models more robust.
Developers should learn and use bagging when working with high-variance models like decision trees, especially in scenarios where model stability and generalization are critical, such as in financial forecasting, medical diagnosis, or any application with noisy data. It is particularly effective for improving the performance of weak learners and is a foundational technique in ensemble methods, often implemented in libraries like scikit-learn for tasks like random forests, which extend bagging with feature randomness.