I am working on UCI breast cancer dataset and trying to find the top 3 features that have highest weights. I was able to find the weight of all features using logmodel.coef_
but how can I get the feature names? Below is my code, output and dataset (which is imported from scikit).
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]
Above code outputs weights array. Using these weights how can I get the associate feature names?
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])
Thanks. I would really appreciate any help on this.
This will do the job:
import numpy as np
coefs=logmodel.coef_[0]
top_three = np.argpartition(coefs, -3)[-3:]
print(cancer.feature_names[top_three])
This prints
['worst radius' 'texture error' 'mean radius']
Note that these features are the top three, but they are not necessarily sorted among themselves. If you want them to be sorted, you can do:
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])
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