Bagged Decision Trees are an ensemble learning method that improves the accuracy of decision trees by combining multiple models. The process involves creating several decision trees using different subsets of the training data, which are generated through a technique called bootstrapping. Each tree is trained independently, and their predictions are averaged or voted on to produce a final result. This approach helps to reduce overfitting, a common issue with single decision trees, by introducing diversity among the models. As a result, Bagged Decision Trees often yield better performance and robustness, making them a popular choice in machine learning tasks.