Both Box-Cox and Yeo-Johnson transform non-normal distribution into a normal distribution. However, Box-Cox requires all samples to be positive, while Yeo-Johnson has no restrictions.

To me, it seems that Yeo-Johnson is superior to Box-Cox. Is there any reason why I shouldn't always blindly use Yeo-Johnson over Box-cox ? (ex: back-transform, interpretability, computation efficiency...)


Interpretability is a major issue.

The power parameter is different for positive and negative values; and the transformation therefore has a different interpretation for positive and negative values.

When you have both, the transformation over the whole range seems somewhat arbitrary and it is very tricky to explain - especially to a lay audience.


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