sorry if this is statistics 101 but i cannot find a similar question. I am wanting to use xgboost to classify my data in two classifications. my data is numerical (financial statement data) and i can see that the distributions for each numerical column is very skewed.

there is a need to remove outliers for xgboost (since based on residuals) so i would like to remove them. my intended method is as follows:

1) transform all columns to normal distribtuon using box cox (not log transform as i have some negative fieds in my columns e.g. -4567.

2)remove outliers using quantile based method..

does the above seem along the right lines?

note i know in practice you should not drop outliers but surely this depends on your model. since xgboost fits a loss function on the residials i think it makes sense to remove these values.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.