# Why use bayesglm?

My overall question is: why use bayesglm instead of other classification methods?

Note:

1. I'm only interested in prediction.
2. I have a decent amount of data (~ 100,000 obs).

I feel like the sample size is large enough the parameters of a regular logistic regression are going to be normally distributed (CLT). What would I gain by specifying priors? My hunch is that it will only matter for a small dataset, but I don't have any theoretical or applied evidence.

• Your intuition about the relationship between sample size and priors is correct. On the other hand, Bayesian logistic regression can solve the problem of infinite parameter estimates resulting from perfect separation. – Sycorax Oct 23 '13 at 20:39
• Logistic regression is not a classification algorithm. It is a probability prediction algorithm. – Brash Equilibrium Jul 23 '19 at 14:17
• What Sycorax mentions is one of the most important reasons you would want to use a Bayesian model in a large-sample setting. If your logistic regression has lots of predictors, especially predictors with low variance, consider having priors over the regression coefficients. – Brash Equilibrium Jul 23 '19 at 14:18