Ensemble Learning is a powerful technique in machine learning that combines multiple models to improve predictive performance. The core idea is to leverage the strengths of various models, often referred to as “weak learners,” to create a more robust and accurate predictive model. This approach is particularly effective because individual models may perform poorly on their own, but when combined, they can produce a strong learner that yields better results.

There are three primary methods of ensemble learning: bagging, boosting, and stacking. Bagging involves training multiple models on different subsets of the data and averaging their predictions to reduce variance. Boosting, on the other hand, focuses on correcting the errors made by previous models by sequentially adding new models that emphasize misclassified instances. Stacking combines different types of models and uses a meta-model to learn how to best combine their predictions ⁴⁵.

Ensemble learning is widely used in various applications, including competitions and real-world problems, due to its ability to enhance accuracy and reduce overfitting.