# is a 4 output regression equivalent to 4 single output regression superposition?

I would like to generate a regression based on 6 different inputs and 4 output.

I use python and I compare sklearn and scipy. But in both cases, regression models are essentially focused on 1 output parameter.

My question is : can I considerer that all of my outputs are unlinked, and if yes, is it true to imagine that I can perform 4 regressions in parallel (one for each output) to make an equivalent 4 ouput model ?

Tank you for your tips !

"My question is : can I consider that all of my outputs are unlinked"

No, in general you can't, unless you have prior knowledge indicating so. There is a high chance that your outputs are correlated. To explore linear correlations, perform a correlation analysis (calculate correlation matrix). To investigate nonlinear relations, calculate the distance correlation.

and if yes, is it true to imagine that I can perform 4 regressions in parallel (one for each output) to make an equivalent 4 ouput model ?

If your analysis proves that there is low correlation between the outputs, only then you can implement 4 independent regression models in parallel with meaningful results.

My suggestion would be to implement a MIMO machine learning model (multiple inputs / multiple outputs), such as a multivariate Recurrent Neural Network.

Hope it helps :)

I found something on sklearn to generate a multi output from simple output regression. here it is: http://scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputRegressor.html

It looks to be a superposition, isn't it ?