A company will send promotional packages for a new product and wants at least 90% of all likely buyers to receive the materials. A linear learner model currently has 80% recall and 75% precision. How should the company retrain the model to prioritize reaching 90% recall?

To prioritize recall, you should directly configure the model to optimize for it. Setting targetrecall to 90% explicitly tells the linear learner to aim for that recall value. The binaryclassifiermodelselectioncriteria should then be set to recallattargetprecision (or precisionattargetrecall if precision was the target) to ensure the model selection process prioritizes models that achieve the desired recall while maintaining reasonable precision. Setting targetprecision to 90% would prioritize precision, not recall. Using more historical data or increasing epochs are general training improvements but don't specifically target a recall objective. normalizelabel and setting the number of classes to 2 are standard classification settings and don't directly influence the recall optimization strategy.