Bagging (Bootstrap Aggregating)
An ensemble learning technique that trains multiple models on randomly sampled datasets and combines their predictions to improve accuracy and reduce variance.
What is Bagging?
Bagging creates multiple training datasets by randomly sampling the original data with replacement. A separate model is trained on each sample, producing several independent predictions. These predictions are then combined, typically through majority voting for classification or averaging for regression. By combining multiple models, bagging reduces the impact of errors made by any individual model.
Why is Bagging Important?
Machine learning models can sometimes be highly sensitive to variations in training data. Bagging helps reduce this variance, improving model stability, accuracy, and generalization. It can also reduce overfitting, particularly when used with models such as decision trees.
Common use cases
Bagging is commonly used in classification, regression, financial forecasting, fraud detection, risk prediction, and ensemble algorithms such as Random Forest.