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With nested CV: Inner loop for model selection, outer loop for performance evaluation. At what level can we optimize a threshold probability (vs. 0.5) to maximize sensitivity or specificity of the classifier?

Do we need to do nested cross-validation within the inner loop for model selection in the inner loop of this inner loop followed by threshold probability selection in the outer loop of this inner loop, followed by application of the model and probability threshold on the outer loop?

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