Showing 601 - 620 results of 145,778 for search '(( 50 ((we decrease) OR (mean decrease)) ) OR ( 10 ((teer decrease) OR (a decrease)) ))', query time: 0.85s 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. …”
  13. 613

    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. …”
  14. 614

    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. …”
  15. 615

    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. …”
  16. 616

    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. …”
  17. 617

    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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