Ensemble learning 8 serves as an exhaustive meta-approach in machine learning, striving to augment predictive performance via the integration of predictions derived from an array of models. Despite the seemingly boundless ensemble configurations that can be tailored for a predictive modeling problem, there are three methods that primarily preside over the domain of ensemble learning. Each of these methods extends beyond being a mere algorithm, evolving into a distinct field of study that has engendered a multitude of specialized methods. The four cardinal categories of ensemble learning methods comprise voting, bagging, stacking, and boosting. It is imperative to not only acquire a profound comprehension of each method but also to contemplate their incorporation in your predictive modeling project.