Ensemble methods
Ensemble methods are a class of Machine learning techniques that combine the predictions of multiple models to produce a more accurate and robust output. This approach is based on the idea that a group of models can outperform a single model, as the errors of individual models can be reduced through averaging or voting. Ensemble methods are widely used in Data science and Web Development to improve the performance of Neural networks and other Machine learning algorithms. They can be applied to various tasks, such as classification, regression, and clustering. Ensemble methods can also be used to address issues like Overfitting and Underfitting in models.