AWS Certified Machine Learning – Specialty — Question 268

A company is planning a marketing campaign to promote a new product to existing customers. The company has data for past promotions that are similar. The company decides to try an experiment to send a more expensive marketing package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to purchase the new product receive the marketing materials.

The company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%.

How should the company retrain the model to meet these requirements?

Answer options

• A. Set the target_recall hyperparameter to 90%. Set the binary_classifier_model_selection_criteria hyperparameter to recall_at_target_precision.
• B. Set the target_precision hyperparameter to 90%. Set the binary_classifier_model_selection_criteria hyperparameter to precision_at_target_recall.
• C. Use 90% of the historical data for training. Set the number of epochs to 20.
• D. Set the normalize_label hyperparameter to true. Set the number of classes to 2.

Correct answer: A

Explanation

To ensure that at least 90% of actual buyers receive the marketing materials, the model's recall must be at least 90%. In Amazon SageMaker's linear learner algorithm, you can enforce this requirement by setting the target_recall hyperparameter to 90% and choosing recall_at_target_precision for the binary_classifier_model_selection_criteria. Adjusting the training data ratio, epochs, or label normalization parameters does not allow for direct optimization of specific recall thresholds.