potentially dumb question: is there a class techniques for loss functions that decrease the penalty of outliers? as opposed to robust techniques that just don't make the penalty as exteme. i realize the same could be done by removing outliers, and that this is only really a problem in gradient related optimization techniques, but could see it being helpful in high-dimension problems where outliers might not be as obvious so as to not update parameters so much cause of these outliers
potentially dumb question: is there a class techniques for loss functions that decrease the penalty of outliers? as opposed to robust techniques that just don't make the penalty as exteme. i realize the same could be done by removing outliers, and that this is only really a problem in gradient related optimization techniques, but could see it being helpful in high-dimension problems where outliers might not be as obvious so as to not update parameters so much cause of these outliers
of course, there is a loss function. Huber loss helps. We covered it in this newsletter here: https://blog.dailydoseofds.com/p/11-powerful-techniques-to-supercharge