python – 如何计算XGBoost包中的功能得分(/ important)？

命令xgb.importance返回由f分数测量的特征重要性图.

这个f分数代表什么,如何计算？

输出：

Graph of feature importance

解决方法:

这是一个度量标准,简单地总结了每个要素被拆分的次数.它类似于R版本https://cran.r-project.org/web/packages/xgboost/xgboost.pdf中的频率度量

它是您可以获得的基本功能重要性指标.

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.

[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

标签：xgboost,python,r,classification,feature-selection
来源： https://codeday.me/bug/20191004/1853267.html