Ensemble learning refers to techniques that combine predictions from multiple models to produce a more robust or accurate output. The underlying idea is that by aggregating the wisdom of multiple “experts”, the ensemble can outperform any single constituent model. Ensembles can be created in various ways: bagging (parallel independent models combined by averaging or voting, e.g., Random Forests), boosting (sequential models where each new model corrects errors of the previous, e.g., AdaBoost, XGBoost), and stacking (models of different types whose outputs are fed into a meta-learner). Ensembles help reduce variance (bagging), bias (boosting), or leverage complementary strengths of different models. A well-known theoretical justification is that if models have errors that are at least somewhat uncorrelated, averaging can cancel out noise. Ensemble methods are among the most powerful approaches in machine learning competitions and practical applications.