This quick tour to Classification uses a well-known dataset for diagnosing breast cancer as either benign (0) or malignant (1). It covers all the basic steps of predictive modeling, including: (1) Loading training and testing data, (2) Creating a model, (3) Evaluating the predictive accuracy of a model, (4) Generating code automatically, (5) Optimizing a model, (6) Simplifying a model, (7) Choosing the set of functions to work with, (8) Choosing different architectures or model plans, (9) Experimenting with different numerical constants, (10) Exploring different fitness functions, and (11) Making predictions with the evolved models. All the video takes are real-time modeling sessions without cuts to give you a feel for how easy it is to model with GeneXproTools. The dataset used throughout the video consists of a total of 524 cases, of which 350 are used for training (that is, for building the models) and 174 for testing the predictive accuracy of the generated models.
