title: Logistic Regression: How to find top three feature that have highest weights?
body: <p>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 <code>logmodel.coef_</code> but how can I get the feature names? Below is my code, output and dataset (which is imported from scikit).</p>
  
  <pre><code>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]
  </code></pre>
  
  <p>Above code outputs weights array. Using these weights how can I get the associate feature names?</p>
  
  <pre><code>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])
  </code></pre>
  
  <p>Thanks. I would really appreciate any help on this.</p>