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dc.contributor.authorChen, Junda
dc.date.accessioned2021-09-07T18:18:43Z
dc.date.available2021-09-07T18:18:43Z
dc.date.issued2021-09-01
dc.identifier.citationTR1869en_US
dc.identifier.urihttp://digital.library.wisc.edu/1793/82299
dc.description.abstractIn this paper, we analyze the classic US Adult Income Dataset using logistics regression and random forest to analyze potential factors that contribute to income bias for the 50Kincome bracket(income ≥ 50K per year). Using the two methods, we train the dataset and obtain stable models overcross validation. We also found that the two methods, although both showing good accuracy, exhibit conflicting interpretation about what factors have the most influence on the US adult income.en_US
dc.relation.ispartofseriesTR1869;
dc.subjectmachine learningen_US
dc.subjectrandom foresten_US
dc.subjectbig dataen_US
dc.subjectlogistics regressionen_US
dc.subjectneural networken_US
dc.subjectfeature engineeringen_US
dc.titleFeature Significance Analysis of the US Adult Income Dataseten_US
dc.typeTechnical Reporten_US


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    Technical Reports Archive for the Department of Computer Sciences at the University of Wisconsin-Madison

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