  In high-dimensional classification settings, we wish to seek a balance
between high power and ensuring control over a desired loss function. In many
settings, the points most likely to be misclassified are those who lie near the
decision boundary of the given classification method. Often, these
uninformative points should not be classified as they are noisy and do not
exhibit strong signals. In this paper, we introduce the Thresholding Method to
parameterize the problem of determining which points exhibit strong signals and
should be classified. We demonstrate the empirical performance of this novel
calibration method in providing loss function control at a desired level, as
well as explore how the method assuages the effect of overfitting. We explore
the benefits of error control through the Thresholding Method in difficult,
high-dimensional, simulated settings. Finally, we show the flexibility of the
Thresholding Method through applying the method in a variety of real data
settings.
