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I am not sure but this could be considered a follow-up case of #17743 which deprecates the "normalize" option for linear models. |
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I was wondering, what is the rational behind forceful de-meaning of each dimension in the variables sets in sklearn's CCA implementation?
https://github.com/scikit-learn/scikit-learn/blob/95119c13a/sklearn/cross_decomposition/_pls.py#L100
Would it not make sense to expose both a) de-meaning and b) unit-variance scaling ('scale' argument) to the user?
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