Ensemble Learning

Ensemble learning studies how to combine predictions from multiple models, from Random Forests and boosting to stacking, voting, and mixtures of experts. Methods may average outputs, learn weights from validation data, or select and route among specialists for each example, with applications ranging from tabular prediction and medical imaging to weather forecasting and language models. Combining models can reduce errors and quantify uncertainty, but gains depend on whether their mistakes are complementary rather than correlated. Current work also weighs accuracy against calibration, computational cost, distribution shifts, and leakage-safe evaluation, while developing theoretical guarantees for aggregation and boosting.