What is the primary characteristic of ensemble learning methods?

Ensemble learning methods are fundamentally characterized by their approach of combining multiple models to enhance overall predictive performance. This technique leverages the strengths of various individual models to create a more robust composite model. By aggregating the predictions from different models, ensemble methods can reduce overfitting, mitigate errors, and improve accuracy compared to any single model operating in isolation.

This collaborative model strategy can take various forms, such as bagging, boosting, or stacking. For example, bagging aims to reduce variance by training multiple models on different subsets of the training data and averaging their predictions, while boosting focuses on converting weak learners into strong ones by sequentially adjusting the weights of misclassified instances.

In contrast to this concept, options that refer to using a single model, training models in isolation, or focusing specifically on feature selection do not encapsulate the essence of ensemble learning, which thrives on the synergy and interaction between multiple models to create a superior predictive framework.