title: How to determine feature importance of non linear kernals in SVM
body: <p>I am using following code for feature importance calculation.</p>
  
  <pre><code>from matplotlib import pyplot as plt
  from sklearn import svm
  
  def features_importances(coef, names):
      imp = coef
      imp,names = zip(*sorted(zip(imp,names)))
      plt.barh(range(len(names)), imp, align='center')
      plt.yticks(range(len(names)), names)
      plt.show()
  
  features_names = ['input1', 'input2']
  svm = svm.SVC(kernel='linear')
  svm.fit(X, Y)
  feature_importances(svm.coef_, features_names)
  </code></pre>
  
  <p>How would I be able to calculate featurue importance of a non linear kernal, which doesn't give expected result in the given example.</p>