What is the primary function of bagging in ensemble learning?

The primary function of bagging, or bootstrap aggregating, in ensemble learning is to create multiple samples of data for training. Bagging involves generating several subsets of the training dataset by sampling with replacement, which means that the same data point can appear in a subset multiple times while some points may not appear at all. This process produces diverse training datasets that are then used to train multiple base models.

The diversity among the models helps in improving the overall performance and robustness of the ensemble when making predictions. By averaging the predictions from these different models, the ensemble can achieve a lower variance, leading to more stable and reliable predictions. Bagging effectively harnesses the power of multiple models to reduce overfitting and enhance performance compared to using a single model trained on the entire dataset.

In contrast to the other options, reducing bias is primarily associated with methods like boosting, improving model interpretability is not a direct purpose of bagging, and normalizing data inputs generally pertains to data preprocessing rather than the specific function of bagging.