I'm working on the multi-class prediction problem, with 6 output classes. These represent different types of land cover. The classification model is pixel-based and I have extracted different attributes from satellite imagery. I have created training dataset from the field data then trained my random forest model (using
ranger R library).
My next step is to predict output classes over the whole study area. But here is the catch: I also want to "guide" the model so that proportions of the classes in the output are predefined. Additionally, I would like to have such constraints defined differently over the study area. For an example, my study area is city-wide and I know class proportions for each of the city-regions. Therefore, I would like that each city-region gets predictions based on its own predefined class proportions.
I'm looking for suggestions about what would be the best way to achieve this.
PS. I'm sorry if explained problem belongs more in the Geographic Information Systems section of the StackExchange, but I'm posting it here since I believe it is more statistically-related.