I was wondering what would be a sensible way to report the results of a penalized logistic regression model in a scientific article (in terms of coefficients, metrics, diagnostics, hyperparameters, etc.)?
With an explanatory (unpenalized) model, I would report things like estimated coefficients, standard errors, degrees of freedom, (pseudo-)R2, p-values, test statistics, perhaps some 2D/3D plot... Here, I'm at a loss.
Some context: I'm working with an ElasticNet model (with
R) but I guess answers apply equally to Ridge or LASSO models. My initial interest was to do inference. Yet, for various reasons (detailed here) I cannot use a "regular" logistic model to identify what explanatory variables drive my outcome so instead, I've decided to build a penalized logistic model in order to find values for my predictors beyond which the probabilities of my outcome cross some threshold values (and/or interpret coefficients directly) and make some quantified recommendations to obtain the desired outcome.