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
这是一个度量标准,它简单地总结了每个功能被拆分的次数。它类似于R版本中的频率度量
这是一个基本的特征重要性度量,你可以得到。
也就是说,这个变量被拆分了多少次?
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]
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