# Outlier treatment

I am working on a regression problem where I have a lot of outliers in multiple variables. As far as I can think of, there are 3 things I can do to outliers.

1. Remove them (least attractive option)

2. Transform them (log transformation, box-cox transformation etc)

3. Do nothing and build a model including them

My question is regarding the second point. If I want to transform my features using any of the transformations solely for the purpose of outlier, is it ok to do it?

• There is a 4th option, use ML model that is less sensitive to outliers, for example Random Forest is less sensitive to outliers than OLS. Nov 18 '21 at 19:43
• Yes I can and will do that but I just had this question that if a model is sensitive to outliers, can I use transformations like mentioned above solely for outlier treatment? Nov 19 '21 at 5:01