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Say you build a machine learning model, like a movie recommender system. You need to optimize for something. You have 1-5 stars as ratings so let’s optimize for mean squared error. Great. Then let’s say you build a new model. It has even lower mean squared error. You deploy it. This model turns out to give a lower mean squared error. You roll it out to users and the metrics are tanking. Crap! Ok so maybe mean squared error isn’t the right thing to optimize for. The way you solve this, of course,...