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In Statistics and Machine Learning, ensemble methods combine multiple learning algorithms to achieve better predictive performance than any single constituent algorithm. Unlike a statistical ensemble in statistical mechanics, which typically involves an infinite number of possibilities, a machine learning ensemble consists of a finite set of models. However, it allows for greater flexibility in the structure of these models, enabling them to work together to improve overall performance.