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thank you we actually had to change the title because we just got this paper back from a reviewer he wanted machine then decide now sexy and weamp;#39;ll get published whereas traditional approaches to poverty targeting tool development privatized in sample performance the purpose of such tools is actually accurate out-of-sample performance because the tools are necessarily applied and a different sample from which theyamp;#39;re developed so our paper demonstrates that poverty targeting tools can be docHubly improved by prioritizing on samples performance we do this by applying cross-validation to a set of tools / me and used by the USA well by US aid partners we select tools based on their performance and cross validation and thus offer substantial improvement over the traditional tools currently being used with pro ties and sample accuracy in addition we explore the performance of several strategic astok and sambal machine learning algorithms including random forests and quan
