Logistic regression is a supervised machine learning algorithm that accomplishes binary classification tasks by predicting the probability of an outcome, event, or observation.

The model delivers a binary or dichotomous outcome limited to two possible outcomes: yes/no, 0/1, or true/false.

A logistic function known as the sigmoid function is used in logistic regression to map predictions and their probability. An S-shaped curve known as the sigmoid function transforms any real value into a range between 0 and 1.

Additionally, the model predicts that the output belongs to that class if the sigmoid function’s output (estimated probability) is higher than a predetermined threshold on the graph. The model predicts that the output does not belong to the class if the estimated probability is less than the threshold.