Logistic regression is a supervised learning algorithm primarily used for classification tasks, particularly when the target variable is categorical. It predicts the probability of a binary outcome (e.g., success/failure, yes/no) by applying the logistic function, which transforms any real-valued number into a value between 0 and 1. This makes it suitable for predicting binary outcomes, such as whether a credit card transaction is fraudulent or not ¹⁴.

Unlike linear regression, which predicts continuous values, logistic regression outputs probabilities that can be mapped to discrete classes. For instance, it can determine whether a student passes or fails based on hours studied and hours slept, where the output is either 0 (fail) or 1 (pass) ³⁴. Logistic regression can also be extended to handle multiclass classification problems using techniques like one-vs-all classification or softmax activation ³⁴.

In summary, logistic regression is a powerful tool for classification problems, leveraging the logistic function to provide interpretable probability scores for different classes.