What does bootstrap aggregating aim to achieve?

Bootstrap aggregating, commonly known as bagging, is a technique in machine learning that focuses on improving the stability and accuracy of a model. It achieves this by using random subsets of the training data that are created through a process called bootstrap sampling, where individual training samples are drawn with replacement.

By training multiple models on these different subsets and then combining their predictions, bagging helps to reduce variance and prevent overfitting, which is often seen in complex models trained on small datasets. The aggregation of several weak models often results in a stronger overall model, leading to improved accuracy. This process stabilizes predictions since it averages out the errors of individual models, leading to enhanced generalization to unseen data.

The other options, while relevant to machine learning concepts, do not aptly describe the specific aim of bootstrap aggregating since it does not primarily focus on reducing model complexity, eliminating bias, or increasing data processing speed.