machine learning

This machine learning method, known as ensemble learning, combines multiple versions of a model that are trained on random subsets of a dataset. The goal is to improve the model’s stability and accuracy by leveraging the strengths of multiple individual models. By aggregating their predictions, ensemble methods can reduce the likelihood of errors and enhance overall performance, often leading to more reliable and robust outcomes.