Case bagging, also known as bootstrap aggregating, is a machine learning technique that involves training multiple models on different subsets of the original dataset and combining their predictions to improve overall accuracy. This method is particularly useful for reducing the variance and increasing the stability of a single model, making it more robust and reliable. By bagging cases, or instances, from the original dataset, this approach helps to reduce the impact of outliers and noise, resulting in more accurate and consistent predictions. Case bagging is commonly used in ensemble learning, where the combined predictions of multiple models often outperform those of a single model.