Logistic regression- is it okay to build a model that maximizes recall and use the coefficients for inference I'm a novice in the field of ML and stats.
So I have a dataset where the target feature (dependent variable) is binary (True, False), I'm trying to make some inferences and find features in the dataset that explain the target feature, I'm not trying to create a predictive model, I'm just trying to get the coefficients and the constant for further interpretations, my current method is to StratifiedShuffleSplit the dataset into a training and a testing sets, then use sklearn RFECV (Recursive feature elimination with cross-validation) with logistic regression (no regularization) and try to maximize the recall
I'm just wondering whether this is a possible way to do inference, or should I just stick with statsmodel logit and use maximum likelihood estimation
if you have other suggestions, please let me know, thanks!

Maximum likelihood estimation exists for a reason.  The gold standard objective function--the one that results in the best coefficients--is the log likelihood plus the penalty function.  The penalty can be a variety of functions.  The best penalty is the log prior, resulting in Bayesian estimates.  No penalty means you get ordinary maximum likelihood estimates, which are fine if your effective sample size (roughly the minimum of the number of outcome events or the number of non-events) is large in comparison with the number of predictor parameters.
Note that split-sample validation is not a good idea unless n>20,000 give-or-take.  Details about all this are in RMS.
