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Lab: Predict a Binary Classification Model
Use Case: Lead Scoring
On this mission, you'll use a strategy called lead scoring to predict the probability that a prospect will become a customer. You will build binary classification models and experiment with datasets of different sizes to determine the optimal amount of data needed for a successful model. You’ll use tools to analyze and compare prediction results and explanations.
Mission format and duration: self-paced, hands-on, 1 hour
Upon completion of this mission, you will be able to:
- Build and evaluate binary classification models that implement lead scoring
- Determine the optimal size of a dataset size using Learning Curves
- Rank and evaluate conversion predictions and explanations
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)
- 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.