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pyGAM: balancing interpretability and predictive power using Generalized Additive Models in Python


With nonlinear models it is difficult to find a balance between predictive power and interpretability. How does feature A affect the output y? How will the model extrapolate? Generalized Additive Models are flexible and interpretable, with great implementations in R, but few options in the Python universe. pyGAM is a new open source library that offers to fill this gap.


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