What is the purpose of bagging in machine learning?

Bagging, which stands for Bootstrap Aggregating, is a technique in machine learning designed to enhance the performance and stability of models. The core idea behind bagging is to generate multiple versions of a training dataset by sampling with replacement (bootstrapping). Each of these datasets is then used to train a separate model.

By combining the predictions of these various models, typically by averaging for regression tasks or majority voting for classification tasks, bagging helps to reduce variance and prevent overfitting. This ensemble approach allows for a more robust and accurate model because it leverages the strengths of multiple learners. Essentially, the aggregation of diverse models leads to improved predictive performance, making bagging particularly effective in scenarios where individual models may perform poorly when evaluated alone.

The other choices do not accurately characterize the main goal of bagging. While it may lead to simplifications in certain contexts, this is not its primary purpose. Similarly, bagging does not aim to reduce data redundancy or increase the size of the training dataset in a meaningful way; rather, it focuses on utilizing the existing data more effectively by creating multiple samples for training.