Ensemble Methods

Ensemble methods are a type of machine learning technique that combines multiple models to improve overall performance. The main idea is to leverage the strengths of various models while mitigating their weaknesses, resulting in more accurate and robust predictions. Common ensemble methods include bagging, boosting, and stacking, each with its unique approach to combining model outputs. These techniques are widely used in various applications, such as classification, regression, and even in competitive machine learning scenarios, where they can significantly enhance predictive accuracy.