In which way does bagging primarily reduce errors in predictions?

Averaging predictions from different models on separate datasets is a fundamental principle of bagging, or Bootstrap Aggregating. Bagging works by creating multiple subsets of the original training dataset through resampling, where some observations may be repeated while others may be omitted. Each of these subsets is then used to train a separate model.

When it comes time to make predictions, the individual predictions from all these models are averaged (for regression tasks) or voted on (for classification tasks) to produce a final result. This process helps in reducing variance because the average of multiple models tends to be more stable than individual predictions made by a single model. By effectively combining the outputs of models trained on diverse data subsets, bagging mitigates overfitting and improves overall predictive performance.

In contrast, other approaches mentioned, like sequentially adding models to correct previous errors, refer more to boosting techniques rather than bagging. Employing a single robust model with multiple tunings does not align with the concept of bagging, as it emphasizes a single model rather than an ensemble of models. Similarly, fitting multiple models on different datasets sounds similar to bagging but misses the crucial aspect of combining predictions, which is key to how bagging reduces errors.