My dataset consists of multiple input variables (X) and multiple output variables (Y).
| X1| X2 | X3 | | Y1| Y2 | Y3 | ---------------------------------- | 1 | 1 | 0 | | 2 | 2 | 0 | | 2 | 2 | 1 | | 3 | 3 | 1 | | 3 | 3 | 3 | | 4 | 5 | 6 |
But, I don't actually want to predict the output variables Y. I want to actually predict the input variables X given Y. I understand that switching the variables X and Y might not be 'optimal' since there could be multiple input values that yield the same output values, but I don't see how else I solve this type of problem. I was planning on using a Random Forest or simple neural net.
From the help I've been trying to get, it seems that I should keep input and output variables as is, but rather use optimization techniques to find the best input(the variable I want to predict) for a given output. I am unsure if my initial intuition on reversing the inputs and outputs would 'get the job done'.
For a more detailed breakdown on the actual problem I am trying to solve, please see this link.