In Machine learning, we usually train models to predict X->Y. For example, a dataset with 20 input features X = (x0, x1, ... x19) and 3 output variables Y = (y0, y1, y2). The number of training/test data usually small, such as <1000 items or even <100 in the training set.
But in industry, the problem I have is that I want to know "how should I set X that I can get Y in a specific range or value"
Does anyone know how to solve this kind of "inverse prediction"? There are two ways in my mind:
The second method might be OK when the Y also have high dimensions. But usually, in my application, Y is very low-dimension and X is very high-dimension, which makes me think method 2 is not very practical.
Does anyone have any new thoughts? I think this should be somehow very common in industry and maybe some people meet similar situation before.
Thank you!
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Perhaps use Bayesian Optimization, where you solve to get the inputs to get you closest to the targeted Y value?
Posted 1 March 2021
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