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Lab: Classify Using Frequency-Severity Modeling
Use Case: Credit Card Fraud
On this mission, you'll use a strategy called Frequency-Severity modeling to classify fraudulent credit card transactions. You’ll create a blender model and compare models using the Model Comparison tool.
Mission format and duration: self-paced, hands-on, 1 hour
Upon completion of this mission, you will be able to:
- Build a fraud-detection regression model using Frequency-Severity modeling
- Compare modeling algorithms using the Model Comparison and Feature Insights tools
- Create a blender model
Who should complete this mission?
- Business Analysts
- Citizen Data Scientists
- Data Scientists
Before embarking on this mission, you should complete one of the following:
- Starter Quest appropriate for your role (self-paced mission)
- AutoML I (virtual instructor-led mission)
- DataRobot for Data Scientists (virtual instructor-led mission)
- Chrome browser
- DataRobot Automated Machine Learning — If you don’t have access to the application, please sign up for our free trial: datarobot.com/trial.