Bagging

Bagging, or Bootstrap Aggregating, is an ensemble machine learning technique designed to improve the stability and accuracy of algorithms. It works by creating multiple subsets of the original dataset through random sampling with replacement, training a separate model on each subset, and then averaging the results or taking a majority vote to produce a final output. This method helps reduce variance and combat overfitting, making it particularly useful for high-variance models like decision trees. Common use cases include improving the performance of classifiers in tasks such as image recognition and fraud detection.