# Robust machine learning for slightly different class proportions in multiple data sets

Say we have n similar data sets, with the same variables, and outcome labels x and y. In these data sets, domains slightly differ as suggested by the proportion of the minority class x (ranging from 1%-15%).

• How can we develop a robust ML algorithm that works out-of-the-box on new data sets with varying proportions of the classes (i.e. 1-99 or 15-85)?
• Which algorithms are a good-fit for this purpose?
• And (how) is this related to transfer learning, domain adaptation and multi-task learning?