我想知道我们是否可以对多类分类问题进行置换特征重要性处理?
from sklearn.inspection import permutation_importance
metrics = ['balanced_accuracy', 'recall']
pfi_scores = {}
for metric in metrics:
print('Computing permutation importance with {0}...'.format(metric))
pfi_scores[metric] = permutation_importance(xgb, Xtst, ytst, scoring=metric, n_repeats=30, random_state=7)
Cell In[5], line 10
8 for metric in metrics:
9 print('Computing permutation importance with {0}...'.format(metric))
---> 10 pfi_scores[metric] = permutation_importance(xgb, Xtst, ytst, scoring=metric, n_repeats=30, random_state=7)
File c:\ProgramData\anaconda_envs\dash2\lib\site-packages\sklearn\utils\_param_validation.py:214, in validate_params.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
208 try:
209 with config_context(
210 skip_parameter_validation=(
211 prefer_skip_nested_validation or global_skip_validation
212 )
213 ):
--> 214 return func(*args, **kwargs)
215 except InvalidParameterError as e:
216 # When the function is just a wrapper around an estimator, we allow
217 # the function to delegate validation to the estimator, but we replace
218 # the name of the estimator by the name of the function in the error
219 # message to avoid confusion.
220 msg = re.sub(
221 r"parameter of \w+ must be",
222 f"parameter of {func.__qualname__} must be",
223 str(e),
224 )
...
(...)
1528 UserWarning,
1529 )
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].
然后我试着使用average='weighted',然后我仍然得到一个错误,说average='weighted'不可用。那么,如何将average='weighted'添加到permutation_importance()中进行多类分类呢?谢谢
from sklearn.inspection import permutation_importance
metrics = ['balanced_accuracy', 'recall']
pfi_scores = {}
for metric in metrics:
print('Computing permutation importance with {0}...'.format(metric))
pfi_scores[metric] = permutation_importance(xgb, Xtst, ytst, scoring=metric, n_repeats=30, random_state=7, average='weighted')
TypeError: got an unexpected keyword argument 'average'
