Bagging, or Bootstrap Aggregating, is an ensemble machine learning technique that improves the stability and accuracy of algorithms by combining multiple models. It works by creating several subsets of the original dataset through random sampling with replacement, training a model on each subset, and then aggregating their predictions to form a final output. This method helps reduce variance and avoid overfitting, making it a crucial strategy in predictive modeling.
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