In which type of problems can bagging be utilized?

Bagging, which stands for Bootstrap Aggregating, is a powerful ensemble learning technique primarily used to improve the stability and accuracy of machine learning algorithms. It is beneficial in both classification and regression problems.

The essence of bagging lies in creating multiple subsets of the original dataset through a process of random sampling, often with replacement. Each subset is used to train an individual model. The outcomes of these models are then aggregated through a simple averaging process for regression tasks or through majority voting for classification tasks. This process helps to reduce variance and combats overfitting, enhancing the overall predictive performance.

By applying this method to both types of problems, bagging can effectively leverage the strengths of numerous base models to achieve more robust and reliable predictions. Therefore, the correct answer encompasses the versatility of bagging in tackling both classification and regression scenarios.