title: Will non-linear regression algorithms perform better if trained with normally distributed target values?
body: <p>After finding out about many transformations that can be applied on the target values(y column), of a data set, such as box-cox transformations I learned that linear regression models need to be trained with normally distributed target values in order to be efficient.(<a href="https://stats.stackexchange.com/questions/298/in-linear-regression-when-is-it-appropriate-to-use-the-log-of-an-independent-va">https://stats.stackexchange.com/questions/298/in-linear-regression-when-is-it-appropriate-to-use-the-log-of-an-independent-va</a>)</p>
  
  <p>I'd like to know if the same applies for non-linear regression algorithms. For now I've seen people on kaggle use log transformation for mitigation of heteroskedasticity, by using xgboost, but they never mention if it is also being done for getting normally distributed target values.</p>
  
  <p>I've tried to do some research and I found in Andrew Ng's lecture notes(<a href="http://cs229.stanford.edu/notes/cs229-notes1.pdf" rel="nofollow noreferrer">http://cs229.stanford.edu/notes/cs229-notes1.pdf</a>) on page 11 that the least squares cost function, used by many algorithms linear and non-linear, is derived by assuming normal distribution of the error. I believe if the error should be normally distributed then the target values should be as well. 
  If this is true then all the regression algorithms using least squares cost function should work better with normally distributed target values. </p>
  
  <p>Since xgboost uses least squares cost function for node splitting(<a href="http://cilvr.cs.nyu.edu/diglib/lsml/lecture03-trees-boosting.pdf" rel="nofollow noreferrer">http://cilvr.cs.nyu.edu/diglib/lsml/lecture03-trees-boosting.pdf</a> - slide 13)  then maybe this algorithm would work better if I transform the target values using box-cox transformations for training the model and then apply inverse box-cox transformations on the output in order to get the predicted values.
  Will this theoretically speaking give better results?</p>