logistic regression - unbalanced classes Let's suppose I have a dataset with classes A and B, with class A occurring in 1% of cases and class B occurring in 99% of cases. Perhaps class A is a loan default.
Suppose I want to "understand" what factors make one class A vs B by fitting a logistic regression to dependent variables X and then looking at model coefficients. Does it make sense to put a higher class_weight on class A, such as putting a weight of 99 on class A and 1 on class B? Or does the intercept already take care of this?
What if the logistic regression has a regularization parameter, would class imbalance matter more in this scenario? (because the model would be more inclined towards a constant "Predict B" model to reduce the penalty on coefficient size). 
I've seen many economics papers in which a unregularized logistic regression is run on data and then the authors interpret coefficient sizes and significance, just wondering how valid this is.
 A: I assume by "factors" you mean which features or variables are "most" important for distinguishing between the 2 classes? I think that you first need to establish a performance metric to check if the results make sense. E.g., on such a dataset, just always predicting the majority class will already give you a classification accuracy of 99% (or error of 1%). Depending on the size of the dataset, you may want to look at ROC auc or precision-recall aucs (maybe F1) to get an idea how well your model discriminates between the classes.
In addition (also depending on the size of your dataset) you may want to cross-validate, e.g., 0.632+ bootstrapping, 10-fold CV, etc to get an idea about the stability of your model. If you have a sufficiently large dataset, regularization (L1 or L2) may give you a good idea of how important certain features are, answering your question "Suppose I want to "understand" what factors make one class A vs B " -- but this only makes sense if a linear model is appropriate given the data, and if there's sufficient stability of your model in CV I'd say.
