There is usually a trade-off between various model evaluation metrics, and you cannot maximise all of them simultaneously. For e.g., if you increase sensitivity (% of correctly predicted churns), the specificity (% of correctly predicted non-churns) will reduce.
Let's say that you are building a telecom churn prediction model with the business objective that your company wants to implement an aggressive customer retention campaign to retain the 'high churn-risk' customers. This is because a competitor has launched extremely low-cost mobile plans, and you want to avoid churn as much as possible by incentivising the customers. Assume that budget is not a constraint.
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