In machine learning, there are various techniques for building models. One of them is ensemble learning that uses a combination of models to achieve better performance. By aggregating different predictions, ensemble models can demonstrate better results than their alternatives. In this blog post, we will talk about how ensemble learning works, review different types of ensemble learning algorithms, and learn about advantages and disadvantages of ensemble learning for various tasks.

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Ensemble learning combines multiple machine learning models to achieve better performance than individual models. The guide covers core techniques including bagging (training models on different data subsets), boosting (sequential training to correct errors), and stacking (using a meta-learner). It explains voting strategies, diversity requirements, and popular algorithms like Random Forest, AdaBoost, and Gradient Boosting. While ensemble methods improve accuracy, robustness, and generalization, they come with trade-offs including computational complexity, reduced interpretability, and dependency on quality base models.

A guide to ensemble learning