Ensemble Learning
A machine learning approach that combines predictions from multiple models to improve overall accuracy, robustness, and generalization.
What is Ensemble Learning?
Ensemble learning trains or combines several individual models, often called base learners, and aggregates their predictions into a final result. Different models may capture different patterns or make different errors, allowing the combined system to compensate for individual weaknesses. Common ensemble techniques include bagging, boosting, and stacking.
Why is Ensemble Learning Important?
A single machine learning model may suffer from bias, variance, or sensitivity to particular training samples. Combining multiple models can improve generalization, reduce prediction errors, and produce more robust results on unseen data. Ensemble methods are therefore widely used when predictive performance is a priority.
Common use cases
Ensemble learning is commonly used in classification, regression, fraud detection, credit scoring, forecasting, medical diagnosis, and recommendation systems.