Ensemble-based classifiers involve combining multiple models to improve predictive performance. This approach is widely used in supervised learning, where the goal is to classify instances into predefined categories. The paper reviews existing ensemble techniques and serves as a tutorial for practitioners interested in building ensemble-based systems. The main idea of ensemble methodology is to combine several individual classifiers to create a more accurate overall classifier. This concept is supported by historical examples, such as the Condorcet Jury Theorem, which suggests that a majority of voters with slightly better than random accuracy can lead to a correct decision. Similarly, Sir Francis Galton's experiment showed that the average of many guesses can be more accurate than any single guess. These examples illustrate the power of combining multiple opinions or predictions. James Michael Surowiecki's "The Wisdom of Crowds" further supports this idea, arguing that under certain conditions, the collective wisdom of a group can outperform individual experts. For a crowd to be wise, it must meet several criteria: diversity of opinion, independence of thought, decentralization of decision-making, and a mechanism for aggregating individual judgments. In supervised learning, a strong learner can produce arbitrarily accurate classifiers, while a weak learner is only slightly better than random. The question of whether weak learners can be combined to create a strong one is central to ensemble learning. The Condorcet Jury Theorem suggests that this is possible if the individual classifiers are independent and have a probability of correctness greater than 0.5. This principle underpins many ensemble methods, including boosting and bagging.Ensemble-based classifiers involve combining multiple models to improve predictive performance. This approach is widely used in supervised learning, where the goal is to classify instances into predefined categories. The paper reviews existing ensemble techniques and serves as a tutorial for practitioners interested in building ensemble-based systems. The main idea of ensemble methodology is to combine several individual classifiers to create a more accurate overall classifier. This concept is supported by historical examples, such as the Condorcet Jury Theorem, which suggests that a majority of voters with slightly better than random accuracy can lead to a correct decision. Similarly, Sir Francis Galton's experiment showed that the average of many guesses can be more accurate than any single guess. These examples illustrate the power of combining multiple opinions or predictions. James Michael Surowiecki's "The Wisdom of Crowds" further supports this idea, arguing that under certain conditions, the collective wisdom of a group can outperform individual experts. For a crowd to be wise, it must meet several criteria: diversity of opinion, independence of thought, decentralization of decision-making, and a mechanism for aggregating individual judgments. In supervised learning, a strong learner can produce arbitrarily accurate classifiers, while a weak learner is only slightly better than random. The question of whether weak learners can be combined to create a strong one is central to ensemble learning. The Condorcet Jury Theorem suggests that this is possible if the individual classifiers are independent and have a probability of correctness greater than 0.5. This principle underpins many ensemble methods, including boosting and bagging.