Question: Explain the concept of bagging in the context of machine learning.

Answer: Bagging (Bootstrap Aggregating) is an ensemble technique where multiple models are trained on random subsets of the training data with replacement. The final prediction is obtained by averaging or voting on individual predictions.

Example:

A Bagged decision tree ensemble, where each tree is trained on a different bootstrap sample of the data.