Logistic regression is a statistical method used to model the probability of a binary outcome, such as success or failure, yes or no, or presence or absence of a condition. Instead of predicting a continuous value like linear regression, it predicts a probability between 0 and 1 by applying a transformation called the logistic (or sigmoid) function. This function takes a linear combination of input variables and maps it into a probability, allowing us to interpret how likely an event is to occur. The model estimates coefficients for each predictor, which indicate how changes in those variables affect the log-odds of the outcome.
One of the main strengths of logistic regression is its interpretability. Each coefficient can be understood in terms of how it increases or decreases the likelihood of the outcome, holding other variables constant. It is widely used in fields like medicine, economics, and social sciences for classification problems, such as predicting disease risk or customer behavior. While it assumes a linear relationship between predictors and the log-odds, it can still perform well in many practical situations, especially when combined with proper feature selection and data preprocessing.