Which approach combines the estimates of multiple models in machine learning?

Ensemble Learning is the approach that combines the estimates of multiple models to improve the overall performance and robustness of predictions. The fundamental idea behind ensemble methods is that by aggregating the outputs of various models, it is possible to achieve better accuracy and reduce the risk of overfitting compared to any single model.

Ensemble techniques include methods like bagging, boosting, and stacking, each of which has its own way of combining the predictions of multiple models. For instance, in bagging, several subsets of the training set are created, and a model is trained on each subset independently. The final prediction is typically made by averaging or voting among these models. Boosting, on the other hand, focuses on training models sequentially, where each new model attempts to correct the errors made by the previously trained models.

In contrast, Deep Learning is primarily concerned with using neural networks to model complex patterns in data and does not inherently involve combining estimates from multiple models. Reinforcement Learning focuses on how agents ought to take actions in an environment to maximize a cumulative reward, which is a different area altogether. Thus, Ensemble Learning stands out as the precise answer in this context, as it directly refers to the technique of combining multiple model predictions.