Ensembles

Ensembles are methods in machine learning that combine multiple models to make better predictions than any single model alone. Think of it like consulting several experts before making a decision; each model offers its perspective, and their combined judgment leads to more accurate and robust results. By aggregating diverse models—such as decision trees or neural networks—ensembles reduce errors, improve reliability, and handle complex data more effectively. Common techniques include averaging predictions or voting, which leverage the strengths of each model to produce a final, more accurate outcome.