A large mobile network operator built a model to predict customers likely to cancel their service so it can offer retention incentives. After testing on 100 customers the model produced the shown evaluation results. Why would this model be acceptable to deploy to production?

The model is acceptable because its high accuracy of 86% indicates it correctly classifies a large proportion of customers. More importantly, the business cost from false positives (incorrectly predicting a customer will churn) is smaller than the cost from false negatives (failing to predict a customer will churn). This means the company would rather offer an unnecessary incentive to a customer who won't churn (lower cost) than miss an opportunity to retain a customer who will churn (higher cost). The other options are incorrect because precision is not the primary metric for this scenario, and the relationship between false positive and false negative costs is crucial for deployment decisions.