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Let's assume I'm modeling a process like:

$$y=log(x1+x2+x3+N)$$

Where $x_i$ are features and $N$ is some error/noise value.

With the way that decision trees work and the way that LightGBM works, will this be modeled correctly? Or will it be necessary for me to train using $e^y$?

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