👉 Ensemble computing is a machine learning technique that involves combining multiple models to improve predictive performance and robustness. Instead of relying on a single model, ensemble methods aggregate the predictions of several individual models, often trained on different subsets of data or using diverse algorithms. This approach helps reduce variance and bias, leading to more accurate and stable predictions. Common ensemble techniques include bagging (e.g., Random Forests), boosting (e.g., AdaBoost, Gradient Boosting), and stacking. By leveraging the strengths of various models, ensemble computing can significantly enhance the overall performance and reliability of machine learning systems.
