Logistic model

A logistic model maps a real-valued score $\eta$ to the probability $p=(1+e^{-\eta }{)}^{-1}$, equivalently $\log (p/(1-p))=\eta$. Logistic regression takes $\eta$ to be an affine function of the predictors.

For an event of probability $p$, the log odds are the natural logarithm of its odds: