I have a dataset of ~4.7K records focused on binary classification with 60 features. class 1 is of 1554 records and class 2 is of 3558 records.
Now I would like to find the risk factors that influences the outcome which is disease present or not. This is a supervised learning problem
I understand that people do matching to ensure that both the classes have similar distribution, so that the comparison results are reliable.
1) I see people usually do matching based on demographics like Age etc. Is it to infer what factors really influence the outcome if we keep Age constant. Am I right to understand this way?
2) If I put all the variables in logistic regression model, doesn't that account for confounding? Why do I have to do matching?
3) Out of 60 features, I would like to do matching based on 4 variables. How do I do this for my full dataset? Is there any python package to do this?
Can someone help me on how to do this?