It is my first question here, so my excuses if my contribution is naïve.
I'm facing a classification problem in which I have a minority class (~100 samples) labeled as "Positive", another class (~100-1000 samples) labeled as "Likely positive", and a third one (~3000 samples) labeled as "Unknown" or "Unlabeled" or, if you wish, "Likely negative". Therefore, my classes are imbalanced and categorical, but have some sort of incremental order.
My variables are binary (presence/absence of a feature).
I'm not an expert on machine learning, but so far I've found R randomForest package most suitable for many of the problems I've addressed. Random Forests seem quite robust and useful for different situations.
Now, given a test set, in the end what I want to know, for each of the samples, is their probability of being positive. The intuition here is that the "Likely Positive" category should "help" somehow in assessing this probability.
How would you guys address this problem?
Thanks in advance!