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Improving the accuracy of these scores leads directly to more revenue, less fraud, and better end-user experiences for our customers. Therefore, we spend a large amount of effort trying to improve our machine learning’s accuracy. This whitepaper on ML Experiments at Sift discusses some lessons we have learned around building an ML experimentation framework that allows us to do this effectively. We’ll start by describing how we minimize bias in offline experiments.