title: How to explain variables weight from a Linear Discriminant Analysis?
body: <p>I'm working on a supervised classification. To begin I would like to find variables which have important weight to discriminate each class. My code is the following : </p>
  
  <pre><code>from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
  
  X = X_train_std[0:1000,:]
  y = y_train[0:1000]
  target_names = classes
  
  lda = LDA(n_components=2)
  X_r2 = lda.fit(X, y).transform(X)
  
  print('explained variance ratio (first two components) with LDA: %s'
    % str(lda.explained_variance_ratio_)) 
  </code></pre>
  
  <p>The result is :</p>
  
  <pre><code>explained variance ratio (first two components) with LDA: [0.64492115 0.24080238]
  </code></pre>
  
  <p>Then I try this : </p>
  
  <pre><code>lda.covariance_
  </code></pre>
  
  <p>And I obtain an error : </p>
  
  <pre><code>AttributeError                            Traceback (most recent call last)
  &lt;ipython-input-28-35184940aba0&gt; in &lt;module&gt;
  ----&gt; 1 lda.covariance_
  
  AttributeError: 'LinearDiscriminantAnalysis' object has no attribute 'covariance_'
  </code></pre>
  
  <p>Do you have any idea to solve that problem? Morever, if you know to create a correlation circle it would be great! </p>
  
  <p>Thank you.</p>