What is the function of the ensemble model?

The ensemble model functions by combining the predictions of multiple individual models to improve the overall predictive performance. This technique leverages the strengths of each model while mitigating their individual weaknesses. By aggregating the outputs—whether through methods like voting, averaging, or stacking—an ensemble approach can enhance the accuracy and robustness of predictions compared to any single model on its own.

In contrast, the option suggesting that an ensemble model provides just a single prediction does not capture the essence of what ensemble methods are designed to do; they are fundamentally about synthesis and integration rather than simplification. Stating that the function is to decrease accuracy is fundamentally incorrect as the intent of ensemble methods is precisely to improve it. Lastly, while one might increase the number of models or algorithms used in ensemble learning, it does not directly increase the number of input variables involved in the modeling process. Ensemble models focus on combining the predictions from different approaches rather than expanding the variable set available for prediction.