Ensemble learning is a method in machine learning where many models work together. Instead of using just one model, it combines several to get better results. Common techniques include bagging, boosting, and stacking. For example, the Random Forest algorithm uses many decision trees to make predictions.
Ensemble learning is useful in fields like finance, healthcare, and e-commerce. In finance, it can help predict stock prices. In healthcare, it aids in diagnosing diseases by analyzing data from different sources. This method increases accuracy and reduces errors. It handles complex data better than single models. Using ensemble learning leads to more reliable predictions, which helps in making good decisions.