What is the characteristic of stacking in ensemble methods?

Stacking is an ensemble method that involves training a new model, often referred to as a meta-model, on the predictions made by multiple base models. This approach capitalizes on the strengths of different algorithms by combining them to produce a single optimized prediction. The idea is that by gathering diverse models—each of which may capture different aspects or patterns of the input data—the ensemble can generate better performance than any individual model could achieve alone.

This contrasts with other methods such as bagging, which typically uses the same type of model and averages their predictions, or boosting, which builds models sequentially, adjusting the emphasis on difficult predictions. Stacking stands out because it explicitly allows for mixing different modeling approaches, leading to potentially improved accuracy and robustness in predictions. Therefore, the integration of various model types is a key characteristic that enhances the overall performance of the final prediction.

The other options do not accurately reflect the key feature of stacking. For example, while it can help reduce overfitting in certain situations, that is not its defining trait. The notion of only using decision trees is inaccurate, as stacking can employ any combination of model types. Similarly, averaging predictions from the same model type describes techniques like bagging rather than stacking, which thrives on model diversity.