Logistic regression estimates the probability that an event occurs, given a set of predictor variables. The logistic function, also known as the sigmoid function, is used to map predicted values to probabilities. The formula is: P(Y=1|X) = 1 / (1 + e^-(β0 + β1X1 + β2X2 + ... + βnXn)) Where:
P(Y=1|X) is the probability of the event occurring (e.g., developing a disease). β0 is the intercept. β1, β2, ..., βn are the coefficients of the predictor variables. X1, X2, ..., Xn are the predictor variables.
