Predictive Modeling for Categorical Targets Using IBM SPSS Modeler v18.1.1 0A0U8G

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

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