What does ensemble modeling refer to in machine learning?

Ensemble modeling in machine learning refers to the approach of combining multiple algorithms or models to improve overall performance. This technique capitalizes on the strengths of various models while mitigating their weaknesses, ultimately leading to more robust predictions. By aggregating the results from different models—whether they are of the same type or different types—ensemble methods enhance accuracy, reduce variance, and improve the generalization of the model on unseen data.

For instance, techniques like bagging and boosting create ensembles by either averaging the predictions of several base models or sequentially improving upon them. This multifaceted approach is particularly valuable, as it often yields better performance than any single model could achieve alone.

In contrast, using one algorithm to achieve results would limit the potential benefits of diversification in predictions. Similarly, implementing a single data source for training restricts the model from leveraging varied perspectives and patterns that may exist in different datasets. Finally, solely utilizing supervised learning methods excludes the potential benefits brought by unsupervised techniques, which can also enhance ensemble methods by integrating insights gained from unlabelled data.