I am running into an issue in which the the target (label collums) of my dataset contain a mixture of binary label (yes/no) and some numeric value label.
The value of these numeric value (resource 1 and resource 2 collumns) experience a large variation margin. Sometime these numeric value can be like 0.389 but sometimes they can be 0.389 x 10^-4 or something.
My goal is to predict the binary decision and the amount of resource allocated to a new user who have input feature 1 (numeric) and input feature 2 (numeric).
My initial though would be that the output neuron corresponding to the 0-1 decision would use logistic regression activation function. But for the neuron that corresponding to the resource I am not quite sure.
What would be the appropriate way to tackle such situation in term of network structure or data pre-processing strategy ?
Thank you for your enthusiasm !