Machine Learning Foundations, Volume 1: Supervised Learning

Chapter 9. Ensemble Methods

Ensemble methods are a powerful set of techniques in machine learning that combine multiple models to create a composite model, which often achieves performance superior to that of any individual model. By aggregating diverse predictions, ensemble methods improve prediction accuracy and reduce the model variance, resulting in more reliable and robust outcomes.

The core idea behind ensemble learning is that different models, each with their own strengths and weaknesses, tend to make errors under different circumstances. By combining these models, an ensemble minimizes the impact of individual errors, leading to superior overall performance.

Ensemble methods consistently yield state-of-the-art results on various supervised ...