title: How are features ranked in RFECV in scikit learn(sklearn)?
body: <p>I used recursive feature elimination and cross-validated (rfecv) in order to find the best accuracy score for several features I had (m =154).</p>
  
  <pre><code>rfecv = RFECV(estimator=logreg, step=1, cv=StratifiedKFold(2),
                scoring='accuracy')
  rfecv.fit(X, y)
  </code></pre>
  
  <p>The rankings (<code>rfecv.ranking_</code>) and associated score(<code>rfecv.grid_scores_</code>) are confusing to me. As you can see from the top 13 features (ranked in the top 10) their ranks are not base on score. I understand ranking has something to do with how and when the feature was excluded in the cross validation process. But then how is the score related to the ranking? I would expect the highest ranked features to have the highest scores.</p>
  
  <pre><code>Features/Ranking/Scores
  b       1       0.692642743
  a       1       0.606166207
  f       1       0.568833672
  i       1       0.54935204
  l       2       0.607564808
  j       3       0.613495238
  e       4       0.626374391
  l       5       0.581064621
  d       6       0.611407556
  c       7       0.570921354
  h       8       0.570921354
  k       9       0.576863707
  g       10      0.576863707
  </code></pre>