python - XGBoost 包中的特征分数（/重要性）是如何计算的？

Question

该命令xgb.importance返回由f score测量的特征重要性图。

这个f 分数代表什么以及它是如何计算的？

输出： 特征重要性图

Answer 1

这是一个指标，它简单地总结了每个特征被分割的次数。它类似于 R 版本中的频率度量。https://cran.r-project.org/web/packages/xgboost/xgboost.pdf

它几乎是您可以获得的基本特征重要性指标。

即这个变量分裂了多少次？

此方法的代码显示它只是在所有树中添加给定特征的存在。

[这里.. https://github.com/dmlc/xgboost/blob/master/python-package/xgboost/core.py#L953][1]

def get_fscore(self, fmap=''):
    """Get feature importance of each feature.
    Parameters
    ----------
    fmap: str (optional)
       The name of feature map file
    """
    trees = self.get_dump(fmap)  ## dump all the trees to text
    fmap = {}                    
    for tree in trees:              ## loop through the trees
        for line in tree.split('\n'):     # text processing
            arr = line.split('[')
            if len(arr) == 1:             # text processing 
                continue
            fid = arr[1].split(']')[0]    # text processing
            fid = fid.split('<')[0]       # split on the greater/less(find variable name)

            if fid not in fmap:  # if the feature id hasn't been seen yet
                fmap[fid] = 1    # add it
            else:
                fmap[fid] += 1   # else increment it
    return fmap                  # return the fmap, which has the counts of each time a  variable was split on

Answer 2

我发现这个答案正确而彻底。它显示了 feature_importances 的实现。

https://stats.stackexchange.com/questions/162162/relative-variable-importance-for-boosting