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?
In epidemiology, the term “exposure” can be broadly applied to any factor that may be associated with an outcome of interest
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