Logistic regression is just a generalized linear model, so there is a linear regression lurking in here whose errors are supposed to be normally distributed, and from which you can get confidence intervals in the usual way.

That is you have a linear predictor (the -theta' * x part), transformed by the inverse of a link function (the logistic function). You can transform prediction +/- confidence interval using this inverse link to get your +/- in probability terms. It won't be symmetric.

Nice writeup of how to do this in R: http://stackoverflow.com/a/14424417/64174

Logistic regression is just a generalized linear model, so there is a linear regression lurking in here whose errors are supposed to be normally distributed, and from which you can get confidence intervals in the usual way.

That is you have a linear predictor (the -theta' * x part), transformed by the inverse of a link function (the logistic function). You can transform prediction +/- confidence interval using this inverse link to get your +/- in probability terms. It won't be symmetric.

Nice writeup of how to do this in R: https://stackoverflow.com/a/14424417/64174