Binomial family in logistic regression I was asked in an interview why do we use the binomial distribution in logistic regression and how is it related to the class that we are predicting? 

Could anyone explain, without any mathematical equations, why do we use binomial instead on any other distribution?
From wikipedia:


  ..., the binomial distribution with parameters n and $\rho$ is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own boolean-valued outcome: a random variable containing a single bit of information: success/yes/true/one (with probability $\rho$) or failure/no/false/zero (with probability $\rho = 1 − \rho$).


So if you know that logistic regression is performed in order to model a binary output variable to some modelling question (i.e. to give 0 or 1, yes or no, etc.), it would make sense to base any probabilistic assumptions on a distribution, which shares this feature. Therefore, a binomial distribution may make sense compared to a continuous distribution, such as a Gaussian or Cauchy.