What is the primary goal of ensemble learning in machine learning?

The primary goal of ensemble learning is to enhance predictive performance by combining predictions from multiple models. This approach leverages the strengths of different models and mitigates their weaknesses, often leading to improved accuracy and robustness in predictions compared to individual models. By aggregating the predictions of various learners—whether they are simple models or complex—ensemble methods can capture more patterns in the data and provide a more reliable output. Common techniques include bagging, boosting, and stacking, which all focus on creating a strong predictive model by utilizing multiple weaker models.

The other choices do not align with the main objective of ensemble learning. While reducing the size of the model or limiting data preprocessing might be desirable in some contexts, they are not the primary goals of ensemble methods. Additionally, increasing model complexity contradicts the essence of ensemble learning, which seeks to enhance performance through collaboration rather than complicating individual models.