model ensembling

Model ensembling is a technique where multiple machine learning models are combined to improve overall prediction accuracy. Think of it like consulting several experts rather than just one; each model offers its own perspective, and by aggregating their outputs—such as averaging or voting—the final decision tends to be more accurate and reliable. This approach reduces the chances of errors that might occur if relying on a single model, providing a more robust and stable prediction outcome. Ensembling leverages the strengths of different models to achieve better performance on complex tasks.