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Take a look at the following three problem statements.

Problem statement $1$ 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

Problem statement $2$: Let's say you are building a cancer detection model with the objective that both the patient who has cancer and the patient who has not cancer can be detected correctly. It can have serious implications if you predict either of the class wrong, i$.$e$.,$ if wrongly detected as "not cancer', the patient will die of cancer, and if wrongly detected as "cancer", the patient will die of chemotherapy.

Problem statement $3$: You have to build an image classification model where $60\%$ of images belong to one class and rest $40\%$ images belong to another class. You have to predict the class of a new image.

Which is the correctly matched model evaluation metric for the above classification models?

Answer Options

Select any one option

Problem statement $1$: Specificity

Problem statement $2.$ Sensitivity

Problem statement $2$: Specificity

roblem statement $3$ Accuracy

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