What do ensemble models aim to achieve in machine learning?

Ensemble models in machine learning are designed to enhance predictive performance by combining the outputs of multiple models. The rationale behind this approach is that different models may capture different patterns or aspects of the data, and by aggregating their predictions, ensemble methods can reduce errors and improve overall accuracy.

By leveraging the strengths of various models, ensembles can account for a wider range of variability in data and mitigate the weaknesses of individual models. This increased robustness typically leads to better generalization on unseen data compared to single models. Techniques such as bagging, boosting, and stacking exemplify how ensemble methods effectively unify predictions to achieve superior results.

The focus on combining predictions rather than reducing computational time, limiting data input features, or standardizing input data sets makes ensemble models a powerful strategy in addressing the challenges of machine learning tasks where accuracy is paramount.