Logistic Regression is a widely used statistical method in machine learning for binary classification problems. Unlike linear regression, which predicts continuous outcomes, logistic regression estimates the probability that a given input belongs to a particular category. It achieves this by applying the logistic function, which transforms the linear combination of input features into a value between 0 and 1. This makes it particularly useful for scenarios such as spam detection, credit scoring, and medical diagnosis, where the outcome is categorical. Logistic regression is valued for its simplicity, interpretability, and effectiveness in handling binary outcomes.