Showing 881 - 900 results of 145,977 for search '(( 10 ((nm decrease) OR (a decrease)) ) OR ( 50 ((we decrease) OR (mean decrease)) ))', query time: 1.06s Refine Results
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    Paeameter ranges and optimal values. by Zhen Zhao (159931)

    Published 2025
    “…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”
  8. 888

    Improved random forest algorithm. by Zhen Zhao (159931)

    Published 2025
    “…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”
  9. 889

    Datasets used in the study area. by Zhen Zhao (159931)

    Published 2025
    “…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”
  10. 890

    Evaluation of the improved random forest model. by Zhen Zhao (159931)

    Published 2025
    “…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”
  11. 891

    Comparison of model metrics. by Zhen Zhao (159931)

    Published 2025
    “…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”
  12. 892

    Flowchart of population spatialization. by Zhen Zhao (159931)

    Published 2025
    “…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”
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