1: Introduction to predictive models for categorical targets

- Identify three modeling objectives

- Explain the concept of field measurement level and its implications for selecting a modeling technique

List three types of models to predict categorical targets

2: Building decision trees interactively with CHAID

- Explain how CHAID grows decision trees

- Build a customized model with CHAID

- Evaluate a model by means of accuracy, risk, response and gain

Use the model nugget to score records

3: Building decision trees interactively with C&R Tree and Quest

- Explain how C&R Tree grows a tree

- Explain how Quest grows a tree

- Build a customized model using C&R Tree and Quest

List two differences between CHAID, C&R Tree, and Quest

4: Building decision trees directly

- Customize two options in the CHAID node

- Customize two options in the C&R Tree node

- Customize two options in the Quest node

- Customize two options in the C5.0 node

- Use the Analysis node and Evaluation node to evaluate and compare models

List two differences between CHAID, C&R Tree, Quest, and C5.0

5: Using traditional statistical models

- Explain key concepts for Discriminant

- Customize one option in the Discriminant node

- Explain key concepts for Logistic

Customize one option in the Logistic node

6: Using machine learning models

- Explain key concepts for Neural Net

Customize one option in the Neural Net node