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説明
What I would you like to be added:
It would be great if NNI had an extension/framework for model selection.
Why is this needed:
Model selection is a very relevant decision for an ML project, arguably more important than hyperparameter tuning.
Without this feature, how does current nni work：
Currently, NNI seems to require you to decide on a model type in advance and then you optimise this model type. For example, you decide to use XGBoost and then you optimise hyperparameters. But you cannot compare / select from all the different possible model types that NNI supports in principle (e.g. models from scikit-learn, Keras, XGBoost etc.). In summary, it seems that NNI currently covers a little bit of feature selection and a lot of hyperparameter tuning; but it does not cover model selection at all.
I could not find any examples where NNI is used for model selection. If I missed something, I'd be thankful for your reply.