# Which predictive model to use for distributions like this

I have a target variable that has the following distribution. I have tried the typical regression models such as logistic regression, ridge regression, catboost regression etc. but I'm thinking that I could use a model that takes into account the features but also some kind of prior knowledge of the distribution of the target variable. Im trying to predict duration in minutes and I’m trying to use machine learning to predict that. Some of my features might have normal or other distributions but do not really know how to use even that. Any suggestions?

• This does not make sense to me, because the regression is modeling conditional distributions, not the marginal (pooled) distribution, of $y$. Do you have some knowledge of the conditional distribution?
– Dave
Sep 22 at 14:04
• I'm confused. You say you're using logistic regression but your target value looks continuous? Sep 22 at 15:41
• @AdriàLuz Logistic regression could make sense for positive integer values (and zero) if we allow the $n$ of the conditional $Binom(n, p)$ distribution to exceed $1$, though that is not the typical way logistic regression is used in a machine learning setting (as a "classifier"). Given the mention of ridge regression and catboost, it seems safe to assume a machine learning context. // Azal, what kind of data are you trying to predict, categorical or numerical? That will help identify the correct model (though my previous remarks about conditional vs marginal still apply).
– Dave
Sep 22 at 15:55
• Thanks @Dave. That's interesting - do you have any resources on this version of logistic regression? Sep 22 at 16:23
• Im trying to predict duration. My y is duration of events in minutes
– azal
Sep 22 at 18:03