voting ensemble

A voting ensemble is a technique in machine learning where multiple models (predictors) are combined to improve overall accuracy. Each model makes its own prediction, and the ensemble "votes" on the final outcome. For example, in a majority voting system, the option with the most votes from individual models is selected as the final decision. This approach leverages the strengths of different models and reduces the chances of errors, often resulting in more reliable and robust predictions than any single model alone.