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What is bagging in machine learning?

Bagging is a learning technique that improves the performance and accuracy of machine learning algorithms.

It is used to deal with bias-variance trade-offs and reduces the variance of a prediction model.

Bagging avoids overfitting of data and is used for both regression and classification models, especially for decision tree algorithms.