Which learning technique deals specifically with the aggregation of predictions from multiple models?

The technique that focuses on the aggregation of predictions from multiple models is Ensemble Learning. This method involves combining the outputs of several different models to improve the overall performance and robustness of predictions. In Ensemble Learning, individual models, also known as base learners or weak learners, are trained separately, and their predictions are merged to generate a final decision, which often yields better accuracy and generalization compared to relying on a single model.

Ensemble Learning can be utilized in several forms, such as bagging, boosting, and stacking, each with its own approach to integrating predictions. For instance, in bagging, multiple versions of a model are trained on different subsets of data and their outputs are averaged or voted upon to produce a final output. In boosting, models are trained sequentially, with each subsequent model focusing on correcting the errors of its predecessors. This collective decision-making process significantly enhances predictive performance in a wide variety of tasks.

The other techniques mentioned serve different purposes. Supervised Learning focuses on learning a mapping from inputs to outputs based on labeled training data; Unsupervised Learning is concerned with finding patterns in data without labeled outcomes; and Reinforcement Learning is about training agents to make a series of decisions based on feedback from their actions in an environment.