Hi. I would like to ask whether sklearn is unsuitable for logistic regression because in the examples for Logistic Regression only statsmodel library was used and if sklearn is suitable how do i go about it also steps carried out on the numerical variables in Linear Regression like assumption check(e.g No multicolinearity) ,normalization(i.e scaling),removal of ouliers where not carried out on the numerical variables for logistic regression, is it because it is unnecessary when performing logistic regression?
Actually in other courses such as Customer Analytics and Python+SQL+Tableau we employ sklearn to perform a logistic regression.
Sklearn is perfectly good for such models, with the only flaw that it does not provide the p-values of the coefficients.
Thanks so much for answering what of Assumption check for logistic regression is there a need for it especially multicollinearity
Hi Buks – yes, indeed. All assumptions need to be satisfied.