I understand one should normalize the features in supervised learning.
Does it ever make sense to normalize the response variable?
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Sign up to join this communityI understand one should normalize the features in supervised learning.
Does it ever make sense to normalize the response variable?
The purpose of normalization is to prevent a subset of features from dominating the behavior of the model. So normalizing the output variable is not critical.
Perhaps if you're implementing the model using low-precision or fixed point math. For example, in an MCU for an embedded system. On a server, desktop or laptop using floats or doubles it's not critical to normalize the output.