# Predicting binary dependent gives non-binary predictions

I am trying to predict the result of an experiment (binary dependent variable) based on a number of continuous independent variables. When I do this using a largish model (9 main effects + 2 factor interaction) it seems to "work" meaning I get a plot where the predicted values are 0/1. However, if I reduce the model to say just two main effects + interaction, I get predicted values ranging from 0 - 1 (and several points in between). I have tried using probit and logit with glm() in R.

With the smaller model I get no warnings / errors. With the larger one I get:

Warning messages:
1: glm.fit: algorithm did not converge
2: glm.fit: fitted probabilities numerically 0 or 1 occurred


What causes this behaviour?

How can I determine which of the main effects and/or interaction terms are the most important (they all have *** in the summary)?

Linear (OLS) regression predicts $\hat \mu_{x_i}$, the mean of the distribution of $Y$ when $X=x_i$. Logistic regression predicts $\hat\pi_{x_i}$, the probability of 'success' when $X=x_i$. It is supposed to give predicted probabilities. If it doesn't give predicted probabilities, something has gone wrong.