🛠️ Scheduled Maintenance | We’ll be undergoing scheduled maintenance and upgrades between 00:00 PST Jan 26th until 00:00 PST Jan 28th. There may be brief interruption of services in that period. We apologize for the inconvenience.

The 365 Data Science team is proud to invite you to our own community forum. A very well built system to support your queries, questions and give the chance to show your knowledge and help others in their path of becoming Data Science specialists.
Anybody can ask a question
Anybody can answer
The best answers are voted up and moderated by our team

Logistic Regression with sklearn

Logistic Regression with sklearn


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?

2 Answers

365 Team

Hi Buks,
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.

10 months