I would like to make a classifier, where I can classify individuals from one hand, and from the other hand, understanding the data better, meaning figuring out which feature, is the most contributing.
I have two datasets, which are comparable; However, the labels for these datasets are somewhat different. Both datasets have samples with NO (healthy) and Yes(Cancer) labels; But one of the main factor, that makes them different is the inter medium labels; Dataset, has one class in between called (medium) while the other dataset has two intermediate labels ( Small risk, High Risk).
Of course, small risk, is just a risk and can lead to cancer, but also can stay healthy; and high risk has more chance to become cancer but might stay just as a risk; Last but not the list, in the other hand, medium in dataset one is basically a combination of small and high risk !
One can arbitrary group high risk together with cancer, and small risk with healthy; or some other way, and exclude samples from the other dataset ...
my question is here; Can I construct a hierarchical model on the response variable and let the classifier share these information among the group WITHOUT any additional grouping ?
I assume here is an example where Bayesian can gives some real help !