title: How to interpret importance of features from _coeffs outputs for multi-class in sklearn.feature_selection?
body: <p>I have a dataset of 150 samples and almost 10000 features. I have clustered the samples in 6 clusters. I have used <strong>sklearn.feature_selection.RFECV</strong> method to reduce the number of features. The method estimate the number of important features  3000 features wit  ~95% accuracy using <em>10-fold CV</em>. However I can get  ~ 92% accuracy using around 250 features (I have plotted using <strong>grid_scores_</strong>). Therefore, I would like to get that 250 features.  </p>
  
  <p>I have checked that question <a href="https://stackoverflow.com/questions/50387089/getting-features-in-rfecv-scikit-learn">Getting features in RFECV scikit-learn</a> and found out to calculate the importances of selected features by:</p>
  
  <blockquote>
    <p>np.absolute(rfecv.estimator_.coef_)</p>
  </blockquote>
  
  <p>which returns an array length of number of important features for binary classifications. As i indicated before, i have 6 clusters and <strong>sklearn.feature_selection.RFECV</strong> does classifiacation <em>1 vs 1</em>. Therefore i get <code>(15, 3000)</code> ndarray. I do not know how to proceed. I was thinking to take dot product for each feature like that: </p>
  
  <pre><code>cofs = rfecv.estimator_.coef_
  
  coeffs = []
  
  for x in range(cofs.shape[1]):
  
      vec = cofs[ : , x]
  
      weight = vec.transpose() @ vec 
  
      coeffs.append(weight)
  </code></pre>
  
  <p>And i get array of <em>(1,3000)</em>. I can sort these and get the results i want. 
  But i am not sure whether it is right and makes sense. I really appreciate any other solutions.</p>