What is an ensemble method in machine learning?

An ensemble method in machine learning is a technique that integrates different models for increased accuracy. This approach relies on the concept that by combining the predictions of multiple models, the overall performance can be enhanced compared to any single model. The fundamental idea is that various models can capture different patterns in the data, and when their predictions are aggregated—whether through voting, averaging, or stacking—the resulting prediction can often produce lower error rates and better generalization to unseen data.

Ensemble methods can take several forms, including bagging, boosting, and stacking, each employing a unique strategy to combine multiple learners. By leveraging the diversity of models, ensemble techniques mitigate the risk of overfitting and improve robustness, making them a powerful tool in a machine learning practitioner’s toolkit.