What does "ensemble learning" involve?

Ensemble learning involves the strategy of combining multiple machine learning models to enhance overall performance. This approach leverages the strengths of various models to achieve better predictive accuracy than any single model might provide on its own. By aggregating the predictions from different models—through methods such as bagging, boosting, or stacking—ensemble methods can reduce errors, improve robustness, and provide more reliable predictions across different data sets.

The essence of ensemble learning lies in its ability to balance out the weaknesses of individual models through collaboration. For instance, if one model is particularly good at handling certain data patterns while another excels at different ones, together they can cover a broader range of possibilities and yield superior results. This makes ensemble methods particularly valuable in complex prediction tasks where individual models may struggle.

In contrast, the other choices describe approaches that do not utilize the benefits of ensemble learning by either relying solely on one model or not integrating multiple models in any collaborative manner.