Logistic regression is a statistical method used for binary classification problems, which predicts the probability of a binary outcome based on one or more predictor variables. It models the relationship between the dependent variable (the outcome) and the independent variables (the predictors) using a logistic function, which outputs values between 0 and 1. Unlike linear regression, logistic regression is specifically designed for situations where the outcome is categorical. Common use cases include medical diagnosis, credit scoring, and marketing response prediction, where the goal is to classify observations into one of two categories. Its interpretability and efficiency make it a popular choice in various fields of data analysis.
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