XGBoost软件包中的功能得分（/重要性）如何计算？ [英] How is the feature score(/importance) in the XGBoost package calculated?

问题描述

命令 xgb.importance 返回以 f得分衡量的功能重要性图。

该 f分数代表什么以及如何计算？

输出：

功能重要性图

解决方案

这是一个指标，它简单地总结了每个功能被分割了多少次。它类似于R版本中的频率指标。 https：// cran .r-project.org / web / packages / xgboost / xgboost.pdf

它与功能重要性衡量标准基本相同。

即该变量被分割了多少次？

此方法的代码表明，它只是在所有树中添加了给定功能。

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

  def get_fscore（self，fmap =''）：
获取每个功能的重要性。
参数
 ---------- 
 fmap：str（可选）
特征图文件
的名称 
树=自已。 get_dump（fmap）##将所有树木转储为文本
 fmap = {} 
表示树木中的树木：##循环浏览树木
以获得tree.split（'\n '）：＃文本处理
 arr = line.split（'['）
如果len（arr）== 1：＃文本处理
c继续
 fid = arr [1] .split（']'）[0]＃文本处理
 fid = fid.split（'<'）[0]＃在大/小（查找变量名）
 
如果fid不在fmap中：＃如果尚未看到功能ID 
 fmap [fid] = 1＃将其添加到
否则：
 fmap [fid] + = 1＃否则递增
 return fmap＃返回fmap，其中包含每次在
   
The command xgb.importance returns a graph of feature importance measured by an f score.

What does this f score represent and how is it calculated?

Output:

Graph of feature importance
解决方案
This is a metric that simply sums up how many times each feature is split on.   It is analogous to the Frequency metric in the R version.https://cran.r-project.org/web/packages/xgboost/xgboost.pdf  

It is about as basic a feature importance metric as you can get. 

i.e. How many times was this variable split on? 

The code for this method shows it is simply adding of the presence of a given feature in all the trees.

[here..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


                        
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