Recently, we showed how to use utility estimates to pick good classifier thresholds. In that article, we used model performance on an evaluation set, combined with estimates of rewards and penalties for correct and incorrect classifications, to find a threshold that optimized model utility. In this article, we will show […]
Estimated reading time: 10 minutes
Authors: John Mount and Nina Zumel Nina and I were noodling with some variations of differentially private machine learning, and think we have found a variation of a standard practice that is actually fairly efficient in establishing differential privacy a privacy condition (but, as commenters pointed out- not differential privacy). […]
Estimated reading time: 9 minutes