Ensemble Bagging

Ensemble bagging is a machine learning technique that improves model accuracy and robustness by training multiple models on different subsets of the data and combining their predictions. Current research focuses on enhancing bagging's performance through hybrid approaches, such as integrating it with boosting algorithms or specific model architectures like convolutional neural networks (CNNs) and Probit Model Trees, and exploring its application in diverse fields including medical image analysis and inventory management. This technique's ability to reduce overfitting, improve generalization, and handle imbalanced datasets makes it a valuable tool across various scientific domains and practical applications requiring reliable predictive models.