我正在使用UCI乳腺癌数据集，并试图找出权重最高的前三个特征。我已经能够使用logmodel.coef_找到所有特征的权重，但如何获取特征名称呢？以下是我的代码、输出和数据集（从scikit导入的）。

from sklearn.model_selection import train_test_splitfrom sklearn.datasets import load_breast_cancerfrom sklearn.linear_model import LogisticRegressioncancer = load_breast_cancer()X_train, X_test, y_train, y_test = train_test_split(    cancer.data, cancer.target, stratify=cancer.target, random_state=42)logmodel = LogisticRegression(C=1.0).fit(X_train, y_train)logmodel.coef_[0]

上述代码输出权重数组。使用这些权重，我如何获取相关的特征名称？

Output:    array([  1.90876683e+00,   9.98788148e-02,  -7.65567571e-02,             1.30875965e-03,  -1.36948317e-01,  -3.86693503e-01,            -5.71948682e-01,  -2.83323656e-01,  -2.23813863e-01,            -3.50526844e-02,   3.04455316e-03,   1.25223693e+00,             9.49523571e-02,  -9.63789785e-02,  -1.32044174e-02,            -2.43125981e-02,  -5.86034313e-02,  -3.35199227e-02,            -4.10795998e-02,   1.53205924e-03,   1.24707244e+00,            -3.19709151e-01,  -9.61881472e-02,  -2.66335879e-02,            -2.44041661e-01,  -1.24420873e+00,  -1.58319440e+00,            -5.78354663e-01,  -6.80060645e-01,  -1.30760323e-01])

谢谢。任何帮助我都会非常感激。

回答：

这将完成任务：

import numpy as npcoefs=logmodel.coef_[0]top_three = np.argpartition(coefs, -3)[-3:]print(cancer.feature_names[top_three])

这将输出

['worst radius' 'texture error' 'mean radius']

请注意，这些特征是前三名，但它们之间不一定是排序的。如果你希望它们排序，可以这样做：

import numpy as npcoefs=logmodel.coef_[0]top_three = np.argpartition(coefs, -3)[-3:]top_three_sorted=top_three[np.argsort(coefs[top_three])]print(cancer.feature_names[top_three_sorted])