I am currently working on a machine learning model using scikit-learn. In my case, I have 12 input features and 21 output targets, and I am using MLPRegressor to fit my data. However, I noticed a difference in the R2 score when using two different approaches: 1. Only MLPRegressor and 2. MLPRegressor in combination with MultiOutputRegressor.
I understand that we use MultiOutputRegressor when dealing with multitargets, as certain regressors can only handle one target. While MLPRegressor can handle multi targets, I am curious about the reason for the observed difference in performance when using MLPRegressor alone versus when using it with MultiOutputRegressor.
Furthermore, I am interested in knowing the cases where we should consider the relations between variables. Could anyone please provide insights on this matter?