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单纯的 Boruta 判断特征的时候会依照二项分布的接受或者拒绝来判定对前面生成的影子特征进行筛选。所以如果把 Boruta 的第二阶段(特征metric排序以及筛选)单独拿出来,其实是可以用别的方案进行替换的,比如 SHAP(这里的引用来源也是我之前推荐的一本电子书《interpretable-ML》,强烈推荐)。也确实有轮子在做这样的事情,我自己找了一个 Kaggle 上的 Tabular Dataset 试了一下独立工作效果不太明显,不过提供另外一种特征筛选的方法来做 ensemble 应该是有提升的(吧)。轮子在这里:BorutaShap https://github.com/Ekeany/Boruta-Shap



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单纯的 Boruta 判断特征的时候会依照二项分布的接受或者拒绝来判定对前面生成的影子特征进行筛选。所以如果把 Boruta 的第二阶段(特征metric排序以及筛选)单独拿出来,其实是可以用别的方案进行替换的,比如 SHAP(这里的引用来源也是我之前推荐的一本电子书《interpretable-ML》,强烈推荐)。也确实有轮子在做这样的事情,我自己找了一个 Kaggle 上的 Tabular Dataset 试了一下独立工作效果不太明显,不过提供另外一种特征筛选的方法来做 ensemble 应该是有提升的(吧)。轮子在这里:BorutaShap https://github.com/Ekeany/Boruta-Shap

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