Hi everyone,
I am new to python and machine learning. I am currently working on a set of data that helps to determine the response of a customer (i.e. will the customer insure with us, or not).
I understand that the performance may sometimes be enhanced by combining different types of mode', and hence would like to seek if anyone has any resource that I could read on to decide if it would be a good approach to combine linear regression with random forest to reduce my model variance.
Would really appreciate if you could provide some advices!
(* Would also be great if you could share resources on whether AUC would be a good performance measure!)
Best regards,
LinLin