Bootstrap aggregating, commonly known as bagging, is an ensemble machine learning technique designed to improve the accuracy and robustness of model predictions. The primary idea behind bagging is to reduce variance and combat overfitting, especially in models that are highly sensitive to fluctuations in the training data, such as decision trees. Here’s how bagging works: 1. **Bootstrapping**: From the original training dataset, multiple subsets of data are created through a process called bootstrapping.
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