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Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments

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Published in:Personnel Assessment and Decisions
Format: Online Article RSS Article
Published: 2022
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container_title Personnel Assessment and Decisions
description
discipline_display Human Resources
discipline_facet Human Resources
format Online Article
RSS Article
genre Journal Article
id rss_article:68289
institution FRELIP
journal_source_facet Personnel Assessment and Decisions
publishDate 2022
publishDateSort 2022
record_format rss_article
spellingShingle Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
Human Resources
General
Human Resources
sub_discipline_display General
sub_discipline_facet General
subject_display Human Resources
General
Human Resources
Human Resources
General
Human Resources
subject_facet Human Resources
General
Human Resources
title Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
title_auth Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
title_full Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
title_fullStr Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
title_full_unstemmed Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
title_short Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
title_sort comparing empirically keyed and random forest scoring models in biodata assessments
topic Human Resources
General
Human Resources
url https://scholarworks.bgsu.edu/pad/vol8/iss1/7