It is important to note that the distinction between uncertainty types holds only within the fixed boundaries of the learning problem, which include the instance space, output space, joint probability distribution, and hypothesis space. A significant shift in the data-generating distribution or a substantial divergence between training and test data can render both the model's predictions and its uncertainty estimates unreliable.