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For a ML library, which would be the arguments in favour to use a new specific function name for the inverse prediction for a model (e.g. a zero-mean/unit variance scaler), something like inversePrediction(mod,Xnew), and which arguments would be in favour to just use a keyword argument to the already employed predict function, something like predict(mod,Xnew;inv=true) ?

Some context:

  • I use only predict(mod) (and eventually predict(mod,Xnew) for models that generalise to new data) for unsupervised models and so-called transformers without distinguishing between them
  • I use camel case
  • MLJ and scikit-learn use inverse_transform
  • I care more to user-friendly than performance
desertnaut
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Antonello
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  • Is this inverse prediction an actual inverse? If so, I'd suggest sticking to some interface, like [InverseFunctions.jl](https://github.com/JuliaMath/InverseFunctions.jl). – phipsgabler Aug 22 '22 at 15:11

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