title: Using cross-validation to calculate feature importance "Some Questions"
body: <p>I am currently working on a project. I already selected my features and want to check their importance. I have some questions if anyone can help me please. </p>
  
  <p>1- Does it make sense if I use <code>RandomForestClassifier</code> with <code>cross-validation</code> to calculate the feature importance?</p>
  
  <p>2- I tried it to calculate the feature Importance using the <code>cross_validate</code> function 
  <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html" rel="nofollow noreferrer">https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html</a> . The function provides the test_score and train_score results. The results I got with a <code>10 Fold cross-validation</code> were as follows:</p>
  
  <p><strong>test_score</strong> <code>[0.99950158, 0.9997231 , 0.9997231 , 0.99994462, 0.99977848, 0.99983386, 0.99977848, 0.9997231 , 0.99977847, 1.]</code> </p>
  
  <p><strong>train_score</strong> <code>[0.99998769, 0.99998154, 0.99997539, 0.99997539, 0.99998154,0.99997539, 0.99998154, 0.99997539, 0.99998154, 0.99997539]</code>, </p>
  
  <p>Can anyone explain these results? And what does it indicate?</p>
  
  <p>3- The <code>cross_validate</code> function has a parameter called <code>scoring</code>, which has different scoring values such as <code>accuracy</code>, <code>balanced_accuracy</code> and <code>f1</code>. What does the <code>scoring</code> parameter do? And what do these values mean? And how should I decide which one to choose? I already read the scikit-learn documentation but wasn't clear to me.</p>
  
  <p>Thank you.</p>