Logistic Regression

Logistic regression is a classification algorithm that maps a linear combination of features to a probability, typically using the sigmoid function for a binary outcome. A threshold converts that probability into a class decision, and changing the threshold trades false positives against false negatives. Despite its name, the method is used for classification rather than ordinary numerical regression. It is computationally efficient, offers interpretable coefficients, and often provides a strong baseline for structured data. Performance can suffer when relationships are strongly nonlinear unless interactions or transformed features are added. Regularization, feature scaling, class balance, calibration, and representative evaluation all influence real-world reliability.