The process of model development has three default models that you can create:
Regression models work well on very linear data. PAD's logistic regression models are a generalization of linear regression models. They represent the predictive model as a formula where the various predictors are added up after multiplication by a coefficient, the resulting outcome being fit through a logistic function that maps the outcomes to a range between 0 to 1. The regression models can be viewed as the coefficients of the formula or as a scorecard.
Decision tree models work well on mid-volume, highly non-linear data. In a decision tree, the predictive model is represented in a tree-like structure, with conditions in each node. Predictors in each node consist of numeric values (in the case of numeric predictors) or list of values (in the case of symbolic predictors).
Bivariate models add bivariate analysis to PAD. PAD can analyze and model the relationship between all possible pairings of the predictors calculating the potential performance of each pair as if the relationship between them was perfectly modeled, identifying the best operators to model the relationship, calculating its predictive performance, as well as the percentage rating of the potential performance.