The method outlined above was for a particular polymer, thickener sub-system. This was applied on all eight sub-systems; however, for the formulations with 𝑃 " (Dehyquart ® CC6) too few formulations were stable, so we could not train an accurate stability classifier. We prepared 174 samples over two months with this polymer, but less than 15% of these formulations were stable. There are some domains, e.g., in finance detecting credit card fraud, where we can work with a heavily imbalanced dataset, for e.g., < 1% of the data may be a fraudulent transaction, yet we can develop a predictive ML model .