Logistic regression is a statistical method used in binary classification to estimate the probability of an outcome variable taking one of two possible values based on independent predictor variables. The core mechanism employs the logistic function (sigmoid) within a linear combination framework, mapping unbounded real-valued inputs into a bounded range between 0 and 1 to satisfy probabilistic constraints that standard linear regression cannot meet for binary data. This approach utilizes Maximum Likelihood Estimation to derive model coefficients, generating odds ratios as exponential functions of these coefficients to quantify the multiplicative change in event probability associated with unit changes in predictors.
Logistic regression is a statistical method used in binary classification to estimate the probability of an outcome variable taking one of two possible values based on independent predictor variables…