Unlike linear regression, where finding parameters involves solving a system of linear equations, parameter calculation for logistic regression requires the solution of a system of nonlinear equations. The equations become nonlinear because each prediction from the logistic regression model has its own estimated variance; the particular variance estimate influences the prediction, while the estimated prediction influences the estimated variance. The only way to find a solution to these nonlinear equations involves using an iterative, gradient-based algorithm.

For finding the parameters to a logistic regression model, the Business Analysis Module supplies two classes: RWLogisticIterLSQ and RWLogisticLevenbergMarquardt. The following sections provide a brief description of the method encapsulated by each class, along with its pros and cons.