Moreover, classic methods often require only a small number of physically meaningful parameters and are based on simple, intuitive principles that can be easily understood and applied by hand to perform quick back-of-the-envelope estimations. This simplicity and ease of use make classic methods more convenient and accessible than machine learning models in many situations.