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For a given dataset, a common way to compare 2 classifiers is to compare their average validation accuracies using cross-validation.

Is it valid to replace the accuracy with other classification metrics that I care more about? For example, say I care about the sensitivity (recall) at a given specificity level (say 0.99). Is it still valid to compare A and B by computing the average sensitivities using cross-validation? (for each fold, train the model, plot the ROC curve, get the recall for specificity=0.99)

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    $\begingroup$ No; this is at the opposite end of the spectrum from decision making. See fharrell.com/post/mlconfusion $\endgroup$ Jun 4, 2022 at 16:26
  • $\begingroup$ It's not a "no" because, in the end, it's a metric for model evaluation just like many others. The validity of the proposed metric is not in question. It's a no if someone thinks it's a good way to do so. $\endgroup$
    – gunes
    Jun 5, 2022 at 7:14

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Yes, you can choose any metric of interest while you're validating your model. This can be a specific metric of your choice, like sensitivity value at a specific specificity as you proposed, if you think that best suits your need; or area under ROC, precision/recall curve, F1-score etc. The space of possible choices is not limited to accuracy.

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