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Precision and recall comparison across feature selection techniques.
Precision and recall comparison across feature selection techniques.
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Figshare
2025-11-21 更新
2026-04-28 收录
特征选择评估
分类模型性能
数据链接:
https://figshare.com/articles/dataset/Precision_and_recall_comparison_across_feature_selection_techniques_/30680080
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资源简介:
Precision and recall comparison across feature selection techniques.
应用场景:
创建时间:
2025-11-21
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The F1 score obtained by six feature selection methods.
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Number of permuted and unpermuted covariates, number of permuted covariates classified as important (false positives, FP), and number of unpermuted covariates classified as important (UI). FP and UI v
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Performance of selected attributes with the two-step feature selection method.
特征选择评估
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The first column lists different cutoffs of stability selection scores.
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Optimal classification accuracy with filtered subsets and IFS.
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Performance results showing the impact of feature selection in virus-host PPI prediction.
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The impact of feature selection in virus-host PPI prediction is evaluated in comparison to the baseline model (i.e. without feature selection). (XLSX)
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