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While building predictive models we often see skewness in the target variable. Then we generally take transformations to make it more normal. We generally do it for linear models and not for tree based models. This actually means that our distribution is not normal, we are deliberately making it normal for prediction. Why do we do that? What is the advantage we get when outcome is normal?

Archit
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  • Please note that (approximate) lack of skewness is not the same as (approximate) normality. The point is that *symmetric* distributions are more economical to describe. It is unrealistic in most applications to expect a symmetrized distribution to be normal. – whuber Feb 03 '17 at 14:50
  • So why do we do transformations before training linear models ? – Archit Feb 04 '17 at 06:36
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    There are many reasons. Some are best appreciated by looking at examples, such as the analysis I describe at http://stats.stackexchange.com/a/74594/919. – whuber Feb 04 '17 at 16:11
  • Frequently it's not necessary to do so (it's the characteristics of the conditional distribution that matter more than the marginal), and there can several possible reasons, but it's often done for misplaced reasons. Can you give a context where this was done, so we might try to figure out why it was done there? – Glen_b Feb 05 '17 at 23:37

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