Choosing a loss function is an important step in setting up a well-designed machine learning task. It’s a choice that requires domain and business context. It also often requires some amount of technical experience. Finally, it’s something you probably don’t want to change too often or at all.
So a up-front, somewhat irreversible decision that requires expertise and weigh-in from multiple disciplines. Super fun, right? Let’s talk about what goes into this kind of decision, what loss functions entail, and then how you can pick the best one. I’ll also have a list of common loss functions toward the bottom.
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An extremely painful, easily missed issue with machine learning products is that their performance will tend to degrade over time. Generally speaking, the best day of a new model’s life is its last day in development. Performance will likely take a hit the moment it hits production and slowly degrade from there. This is totally normally and simply something to prepare for as your data products become more and more highly developed.
The pain of lost opportunity can be subtle or dramatic. We often spend a lot of time developing data sources and inferential products. We struggled to get them to achieve strong performance in our lab tests. After spending all that time, it can be easy to hold high expectations to the model performance. Really, though, lab performance should be thought of as something closer to a soft upper-bound on live model performance.
In practice, model performance can be severely impacted almost immediately. It can slowly degrade over time in ways that are more subtle but leave just as big of a gap. Even a very advanced model can perform only randomly if the context it has been deployed in changes significantly. Finally, model degradation is difficult and expensive to measure in the lab. It’s possible you won’t even know how bad the degradation problem will be until the model is live. It’ll just show up later in the bottom line.
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