Ensemble Learning

Ensemble learning is a machine learning technique that combines multiple models, often referred to as "base learners" or "weak learners," to solve the same problem and improve overall predictive performance. Instead of relying on a single model, ensemble methods harness the power of many models working together, which often results in more accurate, stable, and robust predictions. There are several types of ensemble learning, with the most common being bagging, boosting, and stacking. Bagging (e.g., Random Forest) trains multiple models in parallel on different subsets of the training data and averages their predictions to reduce variance and overfitting. Boosting (e.g., AdaBoost, XGBoost) builds models sequentially, each one correcting the errors of the previous, which helps reduce bias and improve accuracy. Stacking combines different types of models and uses a meta-model to learn how best to blend their outputs. Overall, ensemble learning is widely used because it often outperforms individual models, especially in complex or noisy datasets, and is a key technique in high-performing machine learning systems.