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I am seeking to perform a realtime mapping of M input features to N output parameters where N > M, e.g 2 inputs to 10 outputs. In my use-case I would define regions within my input space and associate parameters in my output space in the training phase, then have the ML infer transitions between these regions in the mapping phase. I guess the opposite of dimensionality reduction!

What would be some good machine learning algorithms for achieving this?

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Neural network can do this but it seems strange to me that you have output dimension greater than the input.

The other way can be training 10 decision trees (or something else), each for one output.

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