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Documentation located at https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_example.html says that "input scaling of channel spends or control features" and "inverse scaling back to target domain" at the time of out-of-sample predictions are taken care of automatically by MMM.
When these statements are viewed together, it becomes confusing for control variables. Does the user need to scale the control variables? Or does MMM take care of scaling control variables internally?
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Documentation located at https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_example.html says that "input scaling of channel spends or control features" and "inverse scaling back to target domain" at the time of out-of-sample predictions are taken care of automatically by MMM.
However, documentation located at https://www.pymc-marketing.io/en/0.6.0/api/generated/pymc_marketing.mmm.delayed_saturated_mmm.DelayedSaturatedMMM.html says that "If control variables are present, we do not scale them! If needed please do it before passing the data to the model."
When these statements are viewed together, it becomes confusing for control variables. Does the user need to scale the control variables? Or does MMM take care of scaling control variables internally?
Kindly clarify.
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