Bounding your ML models: Don’t let your algorithms run wild

The purpose of designing and training algorithms is to set them loose in the real world, where we expect performance to mimic that of our carefully curated training data set. But as Mike Tyson put it, “everyone has a plan, until they get punched in the face.” And in this case, your algorithm’s meticulously optimized performance may get punched in the face by a piece of data completely outside the scope of anything it encountered previously.