As I'm aware, Decision tree is the worst choice for the learner, if the testing/validation data set differs considerably from the Training data set. But I hear that the Neural networks is relatively more robust even in such cases.
What is that special property of the 'Neural Networks' which makes it more accurate in such cases? Please explain.