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In high-dimensional classification settings, we wish to seek a balancebetween high power and ensuring control over a desired loss function. In manysettings, the points most likely to be misclassified are those who lie near thedecision boundary of the given classification method. Often, theseuninformative points should not be classified as they are noisy and do notexhibit strong signals. In this paper, we introduce the Thresholding Method toparameterize the problem of determining which points exhibit strong signals andshould be classified. We demonstrate the empirical performance of this novelcalibration method in providing loss function control at a desired level, aswell as explore how the method assuages the effect of overfitting. We explorethe benefits of error control through the Thresholding Method in difficult,high-dimensional, simulated settings. Finally, we show the flexibility of theThresholding Method through applying the method in a variety of real datasettings.

Calibrating Black Box Classification Models through the Thresholding Method