Q:

R H2O Deeplearning Multinomial Classification

I am working on a multinomial classification model. The model is to predict transition probabilities. Among the variables used, one of them is the current state (one of the classes). As an example say a loan is presently current. It can transition to current, 1 month delinquent, defaulted, or paid off. But it should not transition to 2 months delinquent. In the training data, a current to 2 month delinquent transition does not occur. After training the model, I looked at the model predictions and there were still non-trivial probabilities to states which are known to be zero. Is it possible to enforce zero probabilities when using R’s h2o deeplearning function?

A:

No, there is not currently a way to force zero probabilities for certain classes within the H2O training functions.  The best solution is probably to write some code to manually process the probabilities after-the-fact.

