I have a dataset with 134 attributes and my goal is to build a binary classification model. While exploring the dataset, I found that there was high skewness present in the attributes. I wanted to understand that would the high skewness present in the attributes have any impact on the classification model I'll build.
For example, if there are attributes A,B,C,D,E,F and Y (response variable) and A, C, E and D seem to have high skewness (both towards positive and negative sides) then do the labels which my model generates for response variable Y get impacted because of this skewness.
Please note that I know how to handle class imbalance, and this is not a class imbalance problem. I also know that skewness can be removed using boxcox transformation from the scipy package , but wanted to be sure if this data needs it or not, because from what I've read, skewness has an impact on regression models, and I couldn't find anything about classification.