排列重要性

from sklearn.inspection._permutation_importance import permutation_importance
from sklearn.datasets import load_iris
from sklearn.metrics import get_scorer
from sklearn.linear_model import LogisticRegression

# 排列重要性

X, y = load_iris(return_X_y=True, as_frame=True)

lr = LogisticRegression()
lr.fit(X, y)

permutation_importance(lr, X, y, get_scorer('accuracy'))

# {'importances_mean': array([0.012     , 0.00933333, 0.56666667, 0.16133333]),
#  'importances_std': array([0.0077746 , 0.00326599, 0.04560702, 0.02124984]),
#  'importances': array([[0.02      , 0.        , 0.00666667, 0.01333333, 0.02      ],
#         [0.01333333, 0.00666667, 0.01333333, 0.00666667, 0.00666667],
#         [0.62      , 0.52      , 0.58      , 0.60666667, 0.50666667],
#         [0.2       , 0.15333333, 0.14666667, 0.14      , 0.16666667]])}

https://blog.csdn.net/u012111465/article/details/106813825