What are the Challenges in Using Reduced Scaling Techniques?
While reduced scaling techniques offer significant advantages, they also come with challenges. The accuracy of linear scaling methods and fragment-based methods can be affected by the choice of partitioning and the quality of the lower-level methods used. In the case of machine learning, the quality of predictions depends heavily on the quality and quantity of the training data. Ensuring that ML models are generalizable and not overfitted to specific datasets is another critical challenge.
