As another example, in evaluating the utility of a pattern, we see a notion of lift — how much more prevalent a pattern is than would be expected by chance — recurring broadly across data science. It is used to evaluate very different sorts of patterns in different contexts. Algorithms for targeting advertisements are evaluated by computing the lift one gets for the targeted population. Lift is used to judge the weight of evidence for or against a conclusion. Lift helps determine whether a co-occurrence (an association) in data is interesting, as opposed to simply being a natural consequence
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