Description: A homogeneous ensemble is a machine learning technique that involves combining multiple models of the same type to improve predictive performance. This strategy is based on the idea that by aggregating the predictions of several similar models, variance can be reduced and the overall accuracy of the system can be enhanced. Homogeneous ensembles are particularly effective in situations where a single model may be prone to errors or overfitting, as combining multiple instances of the same model tends to smooth out fluctuations and provide a more robust estimate. Among the most common methods of homogeneous ensembles are Bagging (Bootstrap Aggregating) and Boosting, which use different approaches to combine predictions. In Bagging, multiple models are trained on random subsets of the data, while in Boosting, models are trained sequentially, each correcting the errors of the previous one. This technique is widely used in various data analytics applications where accuracy is crucial, such as disease prediction, financial analysis, and image classification.
