How to build a multiple output regression model?
34 views (last 30 days)
Show older comments
The problem I am trying to solve is to build a regression model that maps "n" independent variables to "m" response variables.
I have nearly 35000 data points for each of the "n" independent variables and I want to build a regression model using this 35000 X n space to obtain relations to 35000 corresponding data points to each of the "m" response variables (35000 X m response space). Note: eveyrthing is a "double" data type
I found 'fitrauto" function for hyper parameter optimzation for each of the output variables individually by choosing the best regression model and optimising the corresponsing parameters. But what I would like to know is if there is an equivalent function that can build and optimize a regression model for my multi-input, multi-output case.
Schematically what i would like to do:
table_with_data=table(var1, var2, ..., varn)
regression_model=awesome_function(table_with_data, {response_variables}) %[hopefully a function similar to fitrauto so that i don't manually need to evaluate different regression models]
here {response variables}=set of variables {var_a, var_b,...var_m}
I would like to know if there is such an awesome_function, if not how could I implement one?
0 Comments
Answers (1)
Vimal Rathod
on 15 Jun 2021
Hi,
Currently there might not be any function like 'fitrauto' for multi-response variable regression but you could create your custom function using a bayesian model to optimize hyperparameters using the following link.
1 Comment
Alejandro Plata
on 5 Jun 2023
If you don't mind me asking, which MATLAB functions support multi-response regression? I have been searching through mathworks and have been entirely unable to find a suitable function. Thank you!
See Also
Categories
Find more on Linear Regression in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!