Bagging, short for bootstrap aggregating, is an ensemble learning technique primarily used to improve the stability and accuracy of machine learning algorithms, particularly decision trees. The core idea behind bagging is to reduce variance by training multiple models on different subsets of the training data and then aggregating their predictions.

The process begins by creating several bootstrapped samples from the original dataset. A bootstrapped sample is generated by randomly selecting observations from the dataset with replacement. For each of these samples, a separate model (often a decision tree) is trained. Once all models are trained, their predictions are combined: for regression tasks, the average of the predictions is taken, while for classification tasks, the most frequently occurring prediction is selected ²³.

Bagging is particularly effective in reducing overfitting, which is a common issue with high-variance models like decision trees. Random Forest is a well-known example of a bagging algorithm that utilizes this technique to enhance predictive performance ⁵.