Logistic Regression

GLM implements binary logistic regression, transforming target values into a probability scale for classification.

Oracle Machine Learning supports binary logistic regression as a Generalized Linear Model classification algorithm. Link and variance functions are the mechanism that allows GLM to handle targets of a regression that departs in known ways from normality. In logistic regression, a link function is used to relate the explanatory variables (covariates) and the expectation of the response variable. Binomial regression predicts the probability of a success by applying the inverse of a specified link function to a linear combination of covariates. The specified inverse link function can be any monotonically increasing function that maps values from the range (-∞, ∞) to [0,1]. The inverse link function is created from cumulative distribution functions (CDFs) of well-known random distributions. The variance has a known functional relationship with the probability, and a binary target probability varies between zero and one. For logistic regression, the variance function is fixed to its known functional relationship with probability. However, there are other options for the link function. The link function not only transforms the target range into a linear-methods-friendly format, but it also represents a target concept. The analyst can use the target concept to interpret a forecast on two scales: the link scale and the transformed scale. The transformed scale in logistic regression is probability.