What do ensemble models primarily aim to produce?

Ensemble models primarily aim to produce a consensus prediction by combining the outputs of multiple individual models. This approach leverages the idea that while individual models may have their own strengths and weaknesses, aggregating their predictions can lead to improved accuracy and robustness. The rationale behind this is that different models may capture different aspects of the data, and their collective decision often results in a more reliable and less biased prediction.

By generating a consensus output, ensemble methods can effectively reduce the variance of predictions, especially in cases where single models may overfit to noise in the training data. This is particularly useful in scenarios where the individual models may disagree; by averaging or voting among them, ensemble techniques can smooth out these discrepancies, ultimately yielding a superior predictive performance compared to any single model.

The goal is not merely to have a diverse set of predictions or to accumulate a large number of models without purpose, nor is it focused solely on achieving a single prediction. Instead, the primary function of ensemble models is to synthesize these individual predictions into a consensus that enhances overall predictive accuracy and reliability.