Ensemble Learning

Ensemble learning aims to improve the accuracy and robustness of machine learning models by combining the predictions of multiple individual models. Techniques such as bagging, boosting, and stacking are commonly used to create ensembles. By leveraging the diversity of different models, ensemble learning can reduce overfitting and improve predictive performance, especially in complex tasks. This approach is widely adopted in competitions and practical applications to achieve better results.