Hi Pypers, Hope you are doing well.
I am doing multi label classification in which my X and Y are sparse matrices with Y properly binarized. I am able to get done with multi label classification with 12338 features. I saved the model and tried and used it for prediction on new data. This is the issue I am facing: - The number of features which are there in the model is quite different from new data. This is because of OneHotEncoding of categorical variables leading to different # of features on training data vs new data. Let me know in what are the ways this can be resolved. Should I make any upstream changes? Regards, Sanant
------------------------------------------------------------------------------
_______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
