Advanced training of machine-learning models usable in control systems and other systems

This invention describes a method for training machine-learning models more effectively, especially when dealing with complex data where some factors have a nonlinear influence. It works by first grouping similar values of these nonlinear factors into clusters. Then, it uses these cluster assignments to create a refined dataset, which is then used to train the machine-learning model. The claims specify that this clustering and training process involves dividing the data into subsets and processing them across multiple worker nodes using separate threads, indicating a distributed computing approach.