Python statsmodel.api logistic regression (Logit)
If the response is on the unit interval interpreted as a probability, in addition to loss considerations, the other perspective which may help is looking at it as a Binomial outcome, as a count instead of a Bernoulli.  In particular, in addition to the probabilistic response in your problem, is there any counterpart to numbers of trials in each case?  If there were, then the logistic regression could be reexpressed as a Binomial (count) response, where the (integer) count would be the rounded expected value, obtained by product of the probability and the number of trials.
So I'm trying to do a prediction using python's statsmodels.api to do logistic regression on a binary outcome. I'm using Logit as per the tutorials.
When I try to do a prediction on a test dataset, the output is in decimals between 0 and 1 for each of the records.
Shouldn't it be giving me zero and one? or do I have to convert these using a round function or something. 
Excuse the noobiness of this question. I am staring my journey.