Logistic regression, also known as logit regression, or logit model is a probabilistic linear model for dichotomous data. The response variable is a binary variable (nominal variable), which means the variable has two categories or two values; True vs. False, or $$1$$ vs. $$0$$, or success vs. failure, with the probabilities of $$\pi$$ and $$1-\pi$$, respectively. Thus, the response variable follows a binomial distribution written as

$y \sim B(\eta,\pi)\text{,}$

where $$\eta$$ is the binomial denominator, which is for a binary variable $$0$$ or $$1$$ and $$\pi$$ is the probability of success.