In Math

If $p$ is probability..

• odds is $\frac{p}{1-p}$.
• The logit (logistic unit) function or the log-odds is $logit(p) = \log \frac{p}{1-p}$ in statistics.
• Logit function makes a map of probability values from $(0, 1)$ to $(-\infty, +\infty)$.
• The logistic function or the sigmoid function is the inverse-logit. ($logistic(x) = logit^{-1}(x) = \frac{1}{1+e^{-x}}=\frac{e^{x}}{e^{x}+1}=p$

In Machine Learning

The vector of raw (non-normalized) predictions that a classification model generates, which is ordinarily then passed to a normalization function. Normalization function could be the sigmoid function in binary-class classification or softmax function in multi-class classification.