I've the following problem. I've a data set that tries to predict whether a given buy event will happen or not (0/1) when a customer sees a certain product, and I've features created for both the customer and the product (I'm excluding the nature of the data set and related matters to keep things simple). I build a classifier (R's random Forest) and conclude the following.
Actual 0 1 Predicted 0 0.97 0.03 1 0.13 0.87
Separately, I've the priors for the probability of a given product being sold. i.e. number of customers buying a certain product divided by number of customers who got to view that product. My question is two fold
1) When I run the model for a given customer and product combination, I get a probability estimate of buy (given by predict function with type="prob"). How do I blend this with the prior knowledge of the sell through rate of the product? 2) Is this even the right approach to take?