Detailed Course Outline
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