I have a sample with 10 independent variables (X1, X2, X3 ....), and multiple output labels (y1, y2, y3).
Here y1 will depend on X1, X2
y2 will depend on X3, X4 and so on.
y1, y2, y3 might or might not be correlated.
Can you please suggest pros and cons of clubbing this into single model, or should i go with multiple models with single output