# How does skewed data affect deep neural networks?

I'm playing around with deep neural networks for a regression problem. The dataset I have is skewed right and for a linear regression model, I would typically perform a log transform. Should I be applying the same practice to a DNN?

Specifically, I'm curious how skewed data affects regression with a DNN and, if it's negatively, are the same methods that would be applied to a linear regression model the right way to go about fixing it? I couldn't find any research articles about it but if you know of any feel free to link them in your answer!

• When you say you have skewed data, what specifically do you mean? Skewed distributions of predictor variables? Skewed distribution of the response variable? Neither of those even matter to linear regression, where if there is a normality assumption (we get Gauss-Markov without such an assumption), it is about the error term, not about the data. I get the feeling that you're using the $\log$ transform when you don't need to in linear regression. – Dave Nov 9 '20 at 17:36
• The context I had in mind was one skewed predictor variable in multivariate regression with the rest of the predictors being normally distributed. – shaye059 Nov 10 '20 at 18:33