python - 逻辑回归: How to find top three feature that have highest weights?

我正在研究UCI乳腺癌数据集，并试图找到权重最高的前3个特征。我可以使用logmodel.coef_找到所有特征的权重，但是如何获得特征名称呢？下面是我的代码，输出和数据集(从scikit导入)。

from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression

cancer = 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]

上面的代码输出weights数组。使用这些权重，如何获得关联要素名称？

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。我真的很感谢在此方面的任何帮助。

最佳答案

这将完成工作:

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

此打印

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

请注意，这些功能是排名前三的功能，但不一定在它们之间进行排序。如果希望对它们进行排序，则可以执行以下操作:

import numpy as np
coefs=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])

关于python - 逻辑回归: How to find top three feature that have highest weights?，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/43576614/