Ensemble learning involves combining multiple models, often referred to as "weak learners," to create a single, more accurate predictive model. The primary goal is to leverage the strengths of each model while mitigating their individual weaknesses. There are several types of ensemble learning techniques, such as bagging, boosting, and stacking.
