This course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing, pass/fail exams, and machine break-down. Students are introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. Students will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.
1: Introduction to predictive models for categorical targets
List three types of models to predict categorical targets
2: Building decision trees interactively with CHAID
Use the model nugget to score records
3: Building decision trees interactively with C&R Tree and Quest
List two differences between CHAID, C&R Tree, and Quest
4: Building decision trees directly
List two differences between CHAID, C&R Tree, Quest, and C5.0
5: Using traditional statistical models
Customize one option in the Logistic node
6: Using machine learning models
Customize one option in the Neural Net node
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