[VHRanger]

Yes in general.

It makes nonlinear relationships linear. Makes the model less sensitive, too. For instance if the data spans several OoM, adding or removing one datapoint in one of those orders can generate a lot of skew before the log-linearization.

It's easy to cast the log back to the original distribution by taking the exponent afterwards.

[SubiculumCode]

As far as I understand directly transforming your data can lead to problems. In any case, its what link functions do better in generalized linear models[1].

[1] https://en.m.wikipedia.org/wiki/Generalized_linear_model