Multi Output Regressor Model Support

[prgarg007]

Hi Team,
I am looking forward for training a multi-output regression model for performing pattern recognition tasks. I am unable to find direct support for it, after reading through the documentation and examples. Is there a way we can make explanations or any workaround for it ?

Expect Input (49 Values) = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,0,36,114,8,101,50,1,140,20,24,70,224,31,161,59,10,88,103,54,69,71,12,168,86,9,57,2,3,40,37,72,123,48,51,136,14,73,52,49,46,109,91,74,47,33,134,66,113,63,141]
Expected Output (7 Values) = [0, 59, 103, 9, 48, 73, 52, 63]

Sample Dataset:
dataset.csv

[paulbkoch]

Hi @prgarg007 -- We do not have support for multi-output models. You can do this though in a general way by constructing individual regression models and combining them using scikit-learn's MultiOutputRegressor. Will that work for your scenario?

https://scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputRegressor.html

[prgarg007]

Hey @paulbkoch Thanks for your response. Unfortunately It won't work for my scenario. Because pattern recognition for my task needs to be done in a single pass. However, I would love to know if you any suggestions for my task, or any alternative libraries which perform similar to Interpret ML.