Suppose that your supervised learning training set is made out of 3 different datasets, merged into a big one. Because of the way each of those was labeled before merging, you might be suspicious that one of them (maybe the smallest one) is more "important" than the other ones, meaning that their labels are more reliable. The others might contain more errors.
How could you weight the most reliable data points for the ML model to pay more attention to them and increase the loss when it makes a mistake on those samples? And is there a simple way to implement this using scikit-learn?