Available Models

Linear Regression attempts to predict the value of an interval response as a linear function of one or more effect variables.

Logistic Regression attempts to predict the probability that a binary or ordinal response will acquire the event of interest as a function of one or more effects.

Generalized Linear Model is an extension of a traditional linear model that allows the population mean to depend on a linear predictor through a nonlinear link function.

Decision Tree creates a hierarchical segmentation of the input data based on a series of rules applied to each observation.

Cluster segments the input data into groups that share similar features.