Matching Accuracy Metrics white paper
Discover quantitative evaluations and how to compute them on an anonymized data set.
Our focus will be on practical methods with intuitive behavior. In the first part, we examine simulation and surrogate methods which use real data with approximate ground truth. Next, we will focus on indirect methods for big data, with separate parts devoted to false positives and false negatives. We conclude with a series of soft indicators that provide bounds on the accuracy for common pain points in data linkage.
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