What does 'bagging' help achieve in ensemble methods?

Bagging, which stands for Bootstrap Aggregating, is a powerful ensemble method that enhances the performance of machine learning models by combining predictions from multiple base models. The primary goal of bagging is to reduce variance and improve the overall stability and accuracy of the algorithm.

In bagging, multiple subsets of the original dataset are created through bootstrapping, where random samples are drawn with replacement. Each of these subsets is used to train a separate base model. When it comes time to make predictions, the outputs of these individual models are aggregated, usually by averaging for regression tasks or voting for classification tasks. This approach helps to smooth out the errors from individual models, leading to a more robust final prediction.

By focusing on error reduction and stabilizing the predictions across models, bagging effectively minimizes overfitting, especially in high-variance models like decision trees. The collective decision-making process of multiple models results in improved accuracy compared to relying on a single model, which is why the ability of bagging to improve stability and accuracy is a key attribute.

Options suggesting minimization of bias, data collection, or simplification of the training process do not accurately capture bagging’s primary function, which is fundamentally about enhancing prediction stability and accuracy through ensemble learning.